AI Engineer

• AI Implementation - Design, build, and maintain all of our AI systems across the company. Everything from internal marketing, sales, finance, and operations systems to local AI systems in our engineering workflows, and the Chamber itself.
• AI Adoption - Educate, Train, Support, and Uplift everyone in the company with their AI adoption. Running office hours, making tutorials, providing resources, sourcing solutions, and more.
• AI Infrastructure - Architect and operate a scalable set of infrastructure that allows non-technical, and technical teams to use AI in a secure and productive manner.
• AI Systems & Agents - In conjunction with departments, design, build and maintain all AI systems and agents. This includes administering and managing our off the shelf AI systems like Claude, Viktor, Cursor, etc. It also includes our custom built Agents, Skills, Plugins, Tools, and more.
• AI Security - You are responsible for creating, maintaining, and enforcing all data, privacy, and security policies across our AI systems and Agents. Organization, Department, and Role based permissions and settings across all our products.
• Other Projects - As this is a technical role, you will also be asked to help build other internal systems like automations, micro apps, and more.

AI Solutions Lead

• Define and maintain enterprise architecture standards, reference patterns, and governance guardrails for AI-enabled solutions moving from pilot to production.
• Establish decision models for build, buy, or integrate choices; model and vendor selection; data access; security; and responsible AI adoption.
• Design scalable architecture patterns for AI-enabled workflows and agentic processes across data, APIs, identity, security, and cloud platforms.
• Test new AI models, LLMs, agent frameworks, and tools to inform enterprise adoption.
• Design proof-of-concepts, define success criteria, and guide solutions from pilot to production.
• Lead architecture reviews and communicate trade-offs, risks, and growth to partners.
• Partner with teams to translate needs into governed, implementation-ready solutions.
• Ensure teams through demos, workshops, and reusable templates to support safe AI adoption.

Principal Architect – Enterprise AI

• Improve operational efficiency, resilience, and process quality by establishing scalable AI automation architecture standards and governance guardrails
• Accelerate transformation delivery and reduce rework through clear design frameworks, decision models, and reusable automation patterns
• Enhance employee and customer experience by reducing manual work, process fragmentation, and automation risk
• Embed AI-first architectural practices that improve automation scale, decision quality, and measurable business outcomes
• Lead evaluation of AI automation, agentic workflow, and transformation initiatives across the organization
• Define architectural decision frameworks and long-term platform strategy for n8n, Boomi, APIs, event-driven orchestration, and AI services to reduce duplication and increase reuse
• Establish governance guardrails for AI-enabled workflows, agents, prompts, models, data flows, and human-in-the-loop controls to ensure scalability, reliability, auditability, and responsible use
• Anticipate growth, compliance, security, and operational risks across automation platforms and transformation programs before they materialize
• Influence cross-functional roadmaps with AI Automation Engineers, BSAs, and business leaders based on architectural trade-offs, platform fit, and measurable outcomes
• Translate enterprise capability needs into clear, implementable technical designs that accelerate AI automation delivery
• Create architectural blueprints for intelligent workflows, agentic process patterns, and cross-system orchestration that reduce ambiguity and minimize rework
• Design secure, scalable, and resilient automation patterns across SaaS systems, internal platforms, APIs, data ecosystems, and AI/ML services
• Drive API-first, event-driven, and human-in-the-loop architecture standards that improve interoperability, control, and long-term maintainability
• Lead architecture reviews and represent AI automation and transformation strategy in executive and cross-functional forums

Principal Enterprise AI Architect

• Build production AI platform capabilities that transform Prologis building, project, asset, and operational data into reusable solutions for construction, procurement, and operations teams.
• Design and implement production AI platform capabilities, including agents, retrieval-augmented generation, tool calling, workflow orchestration, evaluation, and human review.
• Build an AI-native building knowledge platform that links building models, assets, documents, telemetry, and operational context.
• Develop multimodal data pipelines that convert BIM, Autodesk, geospatial, document, and operational data into structured, queryable knowledge.
• Deliver AI workflows supporting design, construction, commissioning, building operations, sustainment, and reinvestment use cases.
• Move priority solutions from prototype through pilot and production with appropriate governance, documentation, evaluation, observability, and operational readiness.
• Establish reusable engineering patterns that enable successful pilots to scale into enterprise platform capabilities.
• Partner with business, technology, security, and operations teams to define workflows, technical scope, implementation plans, rollout approaches, and post-launch improvements.

Lead AI Engineer

• Architect, design and develop RESTful API Endpoints, ensuring high performance, scalability and maintainability.
• Collaborate closely with front-end engineers to build dynamic, responsive, and user-centric web applications that deliver seamless user experiences.
• Define robust data models and database schemas, and manage relational or NoSQL databases with a strong emphasis on data integrity and performance optimization.
• Direct and govern AI coding agents across the delivery lifecycle, setting context, reviewing agent-generated output, and enforcing SEPS delivery standards at every gate.
• Design and maintain agent workflows using agentic coding tools (Claude Code, Cursor, Copilot) and the AI-native development framework, ensuring structured, auditable, and high-quality delivery.
• Configure and maintain prompt engineering patterns, structured output schemas, with rules and guardrails to align agent behavior with SEPS platform standards.
• Trace, debug, and triage agent tool calls, prompt failures, and unexpected outputs, maintaining visibility into AI-assisted delivery at every stage.
• Create and maintain clear, comprehensive documentation for application architecture, design, configuration, and ongoing maintenance.
• Provide accurate task estimations and delivery timelines, contributing to effective sprint planning and execution.
• Participate in the on-call rotation, responding promptly to production issues and ensuring system stability and uptime.
• Mentor and support junior engineers, fostering a culture of AI-native ways of working, learning, and continuous improvement.

Staff Software Engineer, AI Native

• Design, build, test, and deploy AI legal workflows and skills across firm-approved AI tools, supporting use cases such as contract review, due diligence, drafting, research, and summarization
• Partner directly with attorneys and knowledge management professionals to identify high-value use cases, map workflows, and translate practice needs into structured AI solutions
• Develop and maintain retrieval pipelines, document templates, and reusable workflow components with clear, attorney-ready documentation
• Build and execute evaluation frameworks to measure workflow quality, accuracy, and reliability; monitor performance and iterate based on feedback and outcomes
• Serve as a go-to resource for enablement and change management for developed workflows, partnering with the AI Adoption team to drive usage, deliver training sessions and workshops, 1:1 coaching, and develop practical playbooks and materials that support sustained adoption
• Develop and scale best-practice standards, use-case libraries, and playbooks that empower attorneys to responsibly and independently leverage AI tools while maintaining quality and risk controls
• Embed human-review checkpoints and audit mechanisms aligned with Firm governance, confidentiality, and professional responsibility requirements
• Collaborate closely with cross-functional stakeholders including the AI Product team, AI Adoption team, Knowledge & Innovation team, IT, Innovation Practice Group, and Professional Development to ensure aligned delivery and support
• Track adoption, usage, and impact metrics for deployed workflows and recommend refinements or expansion opportunities
• Manage ongoing maintenance of production workflows, including updates to prompts, retrieval sources, and guardrails in response to model and platform changes, user feedback, or evolving practice needs
• Represent the AI Products & Adoption team in internal meetings, committees, and practice group sessions

AI Legal Engineer

• Design, build, and maintain automated AI evaluation pipelines for production LLM applications
• Develop prompt engineering strategies and iterate on prompts and compare LLMs using quantitative evaluation methods
• Build offline evaluation datasets and regression testing frameworks to measure AI performance over time
• Analyze production AI behavior using Python, SQL, and statistical techniques to identify opportunities for improvement
• Design experiments, A/B tests, and benchmarking methodologies for evaluating prompt and model changes
• Develop dashboards and reporting that communicate AI quality, reliability, and performance metrics
• Partner with engineering and product teams to safely deploy and monitor improvements to production AI systems
• Investigate model failures through detailed error analysis and recommend improvements to prompts, evaluation datasets, and workflows
• Help establish best practices for Responsible AI, evaluation methodologies, and continuous model improvement

AI Evaluation Engineer

• Lead the end-to-end system design, architectural framework, and product integration of Workday’s next generation of intelligent agents
• Architect how foundational models are safely and reliably integrated into production-grade software
• Own the design, experimentation, and orchestration of complex agentic workflows
• Be a core champion for Responsible and Governed AI, architecting systems with guardrails for data privacy and predictability
• Balance high-level system design and hands-on execution, solving critical product constraints like latency and reliability

Principal AI Engineer

• Lead the end-to-end system design, architectural framework, and product integration of Workday’s next generation of intelligent agents.
• Architect how foundational models are safely and reliably integrated into functional, production-grade software.
• Own the design, experimentation, and orchestration of complex agentic workflows, translating cutting-edge AI capabilities into enterprise-grade business value.
• Be a core champion for Responsible and Governed AI—architecting systems with strict guardrails for data privacy, predictability, and explainability.
• Balance high-level system design and hands-on execution, solving critical product constraints like latency, cost, and reliability.

Machine Learning Engineer

• Design, develop, and deploy machine learning models and AI solutions that address real-world customer problems, focusing on usability, scalability, and performance.
• Rapidly develop demos, POCs, MVPs, and workflows to showcase new AI/ML capabilities that could be integrated into the product or used to improve existing features based on customer feedback or market research.
• Develop and improve integrations of open-source ML/AI tools (e.g., MLFlow, Spark, LangChain, Kubeflow) within production environments, ensuring seamless operation on platforms like Kubernetes.
• Fine-tune models and algorithms for accuracy, efficiency, and scalability in production settings, including deep learning technologies.
• Translate customer requirements and industry trends into actionable AI/ML solutions that improve product features, data management, and system performance.
• Work closely with product managers, data scientists, and engineering teams to brainstorm, design, and deploy AI/ML solutions, documenting procedures and best practices.
• Lead efforts in integrating emerging AI tools, mentor junior team members, and communicate progress and challenges to leadership.

Senior Applied Machine Learning Engineer

• Own stakeholder relationships across Medbridge’s executive team, working with them to find the problems and AI enabled solutions that can expedite, or remove, the current blockers in the business.
• Design, build, and ship AI-powered features across our internal teams, from the user interface to the backend services and AI workflows behind it.
• Build full-stack applications using AI-native engineering workflows and tools such as Claude, Cursor, Codex, and OpenAI and Anthropic APIs.
• Develop APIs, backend services, and automation pipelines that support intelligent product capabilities and production-ready deployments.
• Apply software engineering, DevOps, and MLOps best practices to the testing, deployment, monitoring, evaluation, and optimization of AI solutions.
• Partner closely with our Product and Engineering teams to turn ambiguous goals into scalable internal products.
• Use AI tooling throughout your workflow (coding, code review, testing, and planning) and help push the team’s adoption forward.
• Research emerging AI technologies, LLM implementation patterns, and modern product engineering frameworks, and bring what works back to the team.
• Instrument what you ship and use usage data and evidence to decide what to build, fix, or kill next.

Product Engineer – AI

• Build statistical models and production systems to balance advertiser performance with business goals.
• Tune optimization parameters, measure internal competition, and model dynamic environments.
• Design and run experiments to validate theories underpinning the mobile ad tech economy.
• Develop applications in the areas of advertiser budget retention and growth, optimal margin allocation, and bidding innovations.
• Collaborate with a team of world-class engineers with diverse backgrounds as well as peers across the broader company (e.g. Operations, GTM).
• Use strong communication skills (verbal and written) to explain statistical and machine learning concepts to both technical and non-technical audiences.
• Be part of an “engineering excellence” culture through state-of-the-art tools, risk-driven testing, explainable systems, and design/code review.

• Support the implementation and maintenance of AI system components including model serving APIs, inference pipelines, and microservices under the direction of senior developers.
• Assist in implementing integrations between LLMs, AI services, and enterprise applications including internal portals, chatbots, and workflow automation platforms.
• Learn and apply retrieval-augmented generation (RAG) concepts including vector stores and knowledge indexes under the guidance of senior team members.
• Assist in translating business requirements into technical specifications, flow diagrams, and implementation plans with supervision.
• Leverage AI-assisted development tools to support Rapid Application Development (RAD) including generating code scaffolding, templates, unit-test stubs, and documentation under established guidelines and patterns.
• Support data preparation activities in collaboration with Data & Analytics; assist in ensuring data quality, lineage, and appropriate transformations for AI-ready datasets.
• Assist in packaging AI components using containerization (Docker) and support deployment automation activities (Kubernetes, serverless patterns) under direction.
• Support CI/CD pipeline activities for model and service delivery; learn and apply model versioning and model registry practices (MLflow, model registry) under supervision.
• Apply company AI governance guardrails under direction: data minimization, PII handling, prompt filtering, content moderation, and privacy and security best practices; support collaboration with Legal, Compliance, and Security as needed.
• Write unit and integration tests for AI components and APIs under supervision; participate in release planning, deployment verification, and post-deployment monitoring; assist in troubleshooting production issues and documenting findings; maintain technical documentation, runbooks, and user guides as assigned; provide on-call support as required.
• Keep current with LLM, generative AI, and MLOps best practices; learn and apply reusable patterns, templates, and components from the Center of Excellence knowledge base.
• Work effectively across multi-disciplinary teams and present technical details to non-technical stakeholders.
• Collaborate with a variety of people with tact, courtesy, and professionalism.
• Maintain regular, dependable attendance and a high level of performance.
• Maintain a high regard for personal safety, the safety of company assets and employees, and the general public.
• Other daily, weekly, monthly, or special projects may be assigned.

AI System Developer I

• The AI System Developer III serves as a senior individual contributor responsible for the design, development, integration, and support of complex AI-enabled solutions, enterprise integrations, and advanced microservices.
• This role operates with a high degree of independence and contributes to solution design decisions, reusable architectural patterns, and the technical maturity of the AI Center of Excellence.
• The position emphasizes deep expertise across retrieval-augmented generation (RAG), multi-agent orchestration, and model fine-tuning, while accelerating enterprise delivery through AI-assisted development and mentoring of less experienced team members.

AI System Developer, Level III

• Shipping, evaluating, securing, and supporting real systems
• Development on agentic AI engineering technology
• Daily or weekly use of AI coding tools
• Model evaluation and quality measurement
• Maintain active relationships with customers to determine business requirements
• Owns the change request process and coordinates with other teams
• Develop and define application scope and objectives
• Performs technical design reviews and code reviews
• Coordinates and manages on-call support and owns the system monitoring process
• Leads multiple or large projects

Senior AI-Native Software Engineer

• Design, develop, and deploy machine learning models and AI-powered applications
• Collaborate with cross-functional teams to identify and implement AI/ML solutions for client needs
• Build and maintain data pipelines, model training workflows, and inference systems
• Evaluate and integrate large language models (LLMs), generative AI tools, and emerging AI frameworks
• Monitor model performance, conduct testing, and iterate on improvements
• Document technical architectures, methodologies, and best practices
• Stay current with AI/ML research and translate advancements into practical applications

• Program-manage the AI agenda Maintaining a single, portfolio-wide view of every AI initiative across all portfolio companies: scope, status, owner, timeline, dependencies, risks, and value-at-stake.
• Run the governance cadence — stage-gates, steering reviews, prioritization, and resource-allocation decisions — in partnership with the CAIO and Vertical Leads.
• Manage cross-initiative dependencies, sequencing, and bottlenecks across companies and partners.
• Drive initiatives through a consistent delivery lifecycle from intake and prioritization through design, build, deployment, and value realization.
• Surface risks and blockers early and drive them to resolution; keep the program honest about what is on track versus at risk.
• Engage credibly with the AI Solution Architect, portfolio-company technical teams, and implementation partners on solution design at a high level — enough to assess feasibility, scope, effort, and risk, and to challenge weak plans.
• Translate between business objectives and technical delivery: ensure initiatives are scoped against sound architecture and realistic timelines, not optimistic assumptions.
• Spot when a proposed approach is over-engineered, under-scoped, or architecturally unsound, and escalate to the Solution Architect appropriately.
• Recognize reuse opportunities — where a solution built for one company can be redeployed elsewhere — and route them into the shared-pattern library.
• Coordinate the ecosystem of external implementation partners, vendors, and internal teams across concurrent initiatives — managing scope, timelines, and handoffs.
• Track partner delivery against milestones and acceptance criteria (set by the Solution Architect), and manage escalations.
• Allocate and balance scarce resources (talent, partner capacity, budget) across competing initiatives in line with firm priorities.
• Define and run the measurement discipline that ties every initiative to financial and operational outcomes; track value captured versus planned.
• Produce clear, decision-useful reporting for the CAIO, firm leadership, and the Investment Committee on portfolio AI progress, value, risks, and pipeline.
• Identify initiatives that are underperforming and drive decisions to fix, re-scope, or stop them.
• Establish and continuously improve the TMO's tools, templates, intake process, and delivery playbook so the program scales as the portfolio grows.
• Capture lessons across initiatives and feed them back into prioritization, standards, and partner management.
• Support diligence and onboarding of new acquisitions into the AI program, ensuring each new company plugs into the established operating model quickly.

AI Transformation Management Office Lead

• Architect and deploy secure, distributed platforms supporting modular, multi-service software environments.
• Design and integrate APIs, microservices, and data pipelines that enable interoperability across mission, simulation, and analytical systems.
• Define and enforce architectural principles, modularity standards, and software best practices across development teams.
• Engineer solutions that bridge cloud, on-prem, and classified enclaves, ensuring interoperability and compliance.
• Engineer cloud-native solutions in AWS GovCloud or hybrid IL4–IL6 environments with strict security and accreditation requirements.
• Implement Infrastructure-as-Code (Terraform, CloudFormation, or CDK) to automate provisioning, configuration, and accreditation workflows.
• Build and maintain CI/CD pipelines that embed security, compliance, and automated testing from development through deployment.
• Ensure all platform designs support zero-trust principles, multi-enclave deployment, and repeatable accreditation (ATO) processes.
• Design or oversee large-scale data ingestion, transformation, and orchestration environments (Kafka, Airflow, Spark, or Databricks).
• Shape data architectures that balance throughput, latency, and cost across distributed mission environments.
• Enable secure and efficient data access and synchronization across analytical, AI-enabled, and simulation workloads.
• Transition software and platforms from prototype to production, ensuring maintainability, observability, and configuration consistency.
• Establish automated testing, release governance, and performance monitoring standards for ongoing sustainment.
• Engineer architectures for redundancy and resilience under degraded or disconnected operational conditions.
• Design and maintain secure enclave architectures, boundary controls, and cross-domain integration solutions.
• Collaborate with cybersecurity and ISSM teams to support ATO and ongoing accreditation automation.
• Integrate emerging technologies, open standards, and modern frameworks into operational systems.
• Lead by example as a hands-on technical authority, guiding design and implementation decisions.
• Mentor software engineers and architects to raise technical standards, code quality, and architectural maturity.

Distinguished AI Software Engineer

• Architect and integrate hybrid AI systems that combine traditional machine learning, deep learning, large language models (LLMs), and retrieval-augmented generation (RAG) pipelines.
• Design and deploy scalable AI architectures including APIs, microservices, and model-serving frameworks that integrate seamlessly with analytic, simulation, or operational systems.
• Lead the full AI/ML lifecycle — from data ingestion and feature engineering through training, deployment, and sustainment within secure DoD environments (IL5/IL6, ATO, GovCloud).
• Engineer event-driven data pipelines and feature stores for both structured and unstructured data, including text, imagery, and simulation outputs.
• Ensure Responsible AI practices by embedding traceability, explainability, and confidence scoring into deployed systems.
• Implement and maintain MLOps pipelines (MLflow, Kubeflow, Airflow, Docker/Kubernetes) to support continuous integration, retraining, and drift detection.
• Transition R&D prototypes into production, optimizing for mission constraints such as limited compute, edge environments, or disconnected operations.
• Provide technical leadership and mentorship, setting standards for model quality, architectural design, and ethical AI deployment across programs.
• Collaborate across engineering, data, and modeling teams to unify FTI’s AI portfolio, ensuring interoperability and reuse across mission systems.
• Support proposal and solution development, providing technical inputs for AI/ML architectures, data strategies, and Responsible AI assurance frameworks.

Distinguished AI/ML Engineer

• Build and maintain shared infrastructure: secure web app hosting infrastructure, MCP/API standards, secret/key management, model selection, guardrails, and a centralized pattern library that all builders across the organization can pull from.
• Own the review, tracking, and advisory process for all corporate AI and automation builds — MCP servers and tool configurations, Claude Skills, API integrations, and agentic workflows — before deployment.
• Partner directly with AI specialists across departments to identify, scope, and build AI and automation solutions that meet high standards for cost, security, and scalability security before they ship.
• Define and maintain the secure intake path for AI and automation requests across the organization, replacing ad-hoc builds and shadow IT with a governed, cost-effective, and scalable process.
• Contribute to the organization’s AI cost efficiency by monitoring, alerting, and setting thresholds on AI spend across usage-based tools and platforms.

Machine Learning Engineer jobs in Colorado

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AI Solutions Lead

Principal Architect – Enterprise AI

Principal Enterprise AI Architect

Lead AI Engineer

Staff Software Engineer, AI Native

AI Legal Engineer

AI Evaluation Engineer

Principal AI Engineer

Principal AI Engineer

Senior Applied Machine Learning Engineer

Product Engineer – AI

Machine Learning Engineer

AI System Developer I

AI System Developer, Level III

Senior AI-Native Software Engineer

AI Engineer

AI Transformation Management Office Lead

Distinguished AI Software Engineer

Distinguished AI/ML Engineer

AI & Automations Lead