AI Engineer

• Design, build, and operate systems in customer-facing production environments
• Translate ambiguous business and customer problems into prototypes & technical specs
• Track AI capabilities and apply them where they create clear leverage
• Own the full application lifecycle: product discovery, experimentation, evaluation, deployment, and monitoring
• Own system design, tradeoffs, and long-term scaling and maintainability
• Build code bases that are legible to agents and other developers; drive the organization forward on tooling
• Partner with product managers to enhance their product vision, including with AI-native solutions

AI Research Scientist

• Engage in relevant research and development (R&D) of AI systems, models, and advanced machine learning algorithms that augment physics-driven modeling and simulation systems
• Explore and implement AI-powered simulation tooling in support of AI workflows through reinforcement learning, multi-agent systems, and hybrid modeling approaches
• Collaborate with research, engineering, and product teams to build AI-powered solutions that meet mission-critical modeling and decision-support needs
• Engage in and support the drafting and review of conference and journal articles and presentations, sharing advances with both internal stakeholders and the wider research community.
• Contribute content to technical invention disclosures, including associated narrative, graphics, and engagements in support of patent development
• Perform additional responsibilities (no more than 10% of duties) in support of the company’s technology and product development initiatives.

AI Research Scientist, Applied AI

• Architect and develop enterprise-scale multi-agent systems leveraging LLMs and autonomous agent frameworks using Google ADK, Agentspace, MCP, RAG, and A2A orchestration
• Design and implement RAG pipelines using BigQuery and Vertex AI Engine for knowledge grounding and factually accurate responses
• Optimize agents for orchestration, knowledge grounding, multi-step reasoning, and decision-making
• Design and implement distributed training workflows, online inference systems, and low latency serving architectures optimized for real-world performance, using Google cloud-native services
• Engineer scalable, secure, compliant and production-grade AI fabric and AI agent workflows using Vertex AI and modern cloud-native technologies
• Create reusable agent orchestration layers, observability hooks, and governance frameworks that accelerate Agentic AI adoption across TAG brands
• Partner with cross-functional stakeholders in translating business requirements into technical specifications
• Own the full AI development lifecycle – from data collection and implementation to deployment and monitoring
• Implement intelligent observability and automation strategies to ensure AI system reliability and performance at scale

Staff AI Engineer

• Explore and evaluate emerging AI tools, platforms, and agent-based solutions to identify opportunities for the business.
• Design, build, and integrate AI-powered workflows, services, and applications into enterprise systems.
• Help shape and develop internal AI platforms, including agent architectures, permissions, and governance guardrails.
• Guide AI initiatives through structured evaluation and approval processes, ensuring alignment with technical and governance standards.
• Collaborate with teams across Architecture, Security, DevOps, Legal, Privacy, and the wider business to deliver well-rounded AI solutions.
• Translate complex or ambiguous business needs into clear technical requirements, success metrics, and deliverables.
• Monitor performance, adoption, and usage of AI solutions, identifying trends, risks, and opportunities for optimisation.
• Maintain clear documentation, including AI use cases, tool registries, and lifecycle records, ensuring audit-ready standards.
• Support the development of a centralised AI knowledge base to drive consistency and accelerate adoption across teams.
• Contribute to continuous improvement of AI governance, development practices, and delivery frameworks.

AI Engineer, AI Analyst

Machine Learning Engineer

• Design and develop AI-powered systems using both traditional ML and generative AI techniques
• Build intelligent applications and agents that can perform tasks autonomously or interactively
• Create modular, well-documented APIs and service components to enable model integration and consumption
• Develop and manage data pipelines to support model training
• Implement observability and evaluation mechanisms to monitor AI system behavior
• Collaborate with software engineers, data scientists, and solution architects
• Support rapid experimentation and robust deployment pipelines
• Stay informed on the latest trends in GenAI and related technologies
• Support project management tasks, including planning and tracking deliverables

AI/ML Engineer – Senior Associate

• Design and develop agent components and templates for the Collaboratory
• Lead small-scale implementation projects, coordinating with stakeholders and team members
• Create and maintain standardized documentation and “recipe cards” for AI solutions
• Conduct quality assurance testing and debugging of agent components
• Analyze usage metrics and user feedback to improve solution offerings
• Facilitate user training sessions and support enablement events
• Maintain version control documentation and update logs for Collaboratory items
• Assist in packaging common support requests into reusable agent components
• Support junior analysts and contribute to team knowledge sharing

GenAI Engineer – Associate

• Designing, building, and delivering highly scalable big data and ETL solutions across distributed environments.
• Supporting projects at various and unanticipated client worksites throughout the United States.
• Working closely with cross-functional teams to develop, test, and deploy data solutions that meet business and operational needs.
• Leveraging modern big data frameworks, cloud platforms, and agile development practices.
• Preparing technical design documentation for implemented solutions.
• Identifying and resolving data pipeline issues and providing timely solutions.

VP II, AI, Machine Learning

• Contribute to the technical development of cutting-edge agentic frameworks designed to automate the discovery of novel prognostic and predictive models in oncology
• Responsible for building and refining "deep agents" capable of hypothesis generation, experimental design, and multimodal ML modeling utilizing foundation models
• Key technical contributor, working closely with senior scientists and engineers to implement system designs and ensure code quality
• Apply advanced scientific methodologies to develop new predictive models and utilize causal inference frameworks to analyze vast multimodal oncology data
• Collaborate with Research, Engineering & Data Science teams across Tempus’ expansive data science community to develop and deliver innovative computational solutions
• Work with leading pharmaceutical companies to identify where the Tempus platform can add value
• Skillfully navigate client interactions to extract and communicate impactful insights driving new R&D opportunities

Translational Scientist, Applied Machine Learning, Agentic AI

• Evaluate emerging AI tools, platforms, and agents
• Conduct structured technical assessments
• Build and integrate AI workflows and services into enterprise systems
• Collaborate with Architecture, Security, DevOps, Legal, Privacy, and business stakeholders
• Monitor adoption metrics, cost and consumption trends, quality indicators, and post-release performance

AI Engineer / AI Analyst

• Architect the ranking backbone for query understanding and personalization
• Design and build a search autosuggest system
• Develop production-grade Multi-Task Learning
• Own the inference layer for quality optimization
• Advance evaluation practices for long-horizon metrics
• Partner across teams to translate business goals into ranking policies
• Mentor ML engineers in ranking expertise

Senior Machine Learning Engineer II, Search & Recommendations Ranking

• Rapidly prototype AI agents and guided workflows that solve concrete customer problems; ship thin slices, instrument, and iterate with real usage.
• Design and implement cloud-native backend services that orchestrate prompts, tools, retrieval, and automations; own the path from POC to production.
• Build robust retrieval for LLMs (search/vector pipelines, context assembly, tool selection) in partnership with data engineering.
• Establish evaluation loops: offline/online tests, golden sets, regression checks, and human-in-the-loop review to measurably improve quality and safety.
• Contribute to developer experience: reusable agents/tools, SDKs, templates, and internal documentation that speed up future builds.
• Collaborate with customers and internal stakeholders to frame problems, define success metrics, and translate insights into productizable solutions.
• Lead design discussions and code reviews; mentor teammates and raise the quality bar while keeping momentum high.

• Develop and maintain machine learning models that are integral to our production decision-making system and directly influencing business outcomes.
• Adopt or build new technologies for training and serving ML models (e.g. support large models, increase developer velocity, etc)
• Monitor the latest ML research for functional ideas that the team could try.
• Optimize ML Pipelines – Build and scale efficient pipelines for real-time and batch processing.
• Model Monitoring & Improvement – Track performance, detect drift, and automate retraining.
• Use strong communication skills (verbal and written) to explain statistical and machine learning concepts to both technical and non-technical audiences
• Collaborate with a team of world-class engineers with diverse backgrounds.
• Be part of an “engineering excellence” culture through state-of-the-art tools, risk-driven testing, explainable systems, and code review.

Machine Learning Engineer, Conversion ML

• Lead the development of advanced AI and machine learning systems
• Design end-to-end AI solutions, optimize model performance
• Mentor junior engineers and support cross-team collaboration
• Document architectural decisions and best practices

Senior AI Engineer

• Define and own end-to-end enterprise architecture for a Microsoft-based customer engagement and CCaaS platform
• Establish target-state architecture across sales, service, channels, integrations, data, and security
• Develop architecture principles, standards, patterns, and governance guardrails
• Architect solutions across Dynamics 365 Sales, Customer Service, and Omnichannel
• Design end-to-end customer and seller journeys including Lead-to-opportunity, Opportunity-to-close, Account management, Case management and resolution
• Define architecture for: Sales automation, Case management and service workflows, Routing, queues, workstreams, and skills-based distribution
• Architect solutions leveraging Power Platform, Microsoft Teams and Microsoft 365
• Define patterns for integration with third-party and ISV platforms
• Define AI governance including prompt design, security, and responsible AI controls
• Organize and oversee architecture authority across programs and workstreams.

Dynamics 365 Enterprise AI Architect

• Work as senior technical role on a delivery pod
• Review what the AI produces
• Decide what to build

• Implement AI-powered services and application features that integrate enterprise data and systems with LLMs and ML models
• Contribute to solution design by proposing approaches, identifying dependencies, and documenting tradeoffs and implementation plans
• Deliver features using strong engineering practices: test automation, code review, CI/CD, and operational readiness
• Build and tune RAG pipelines and implement prompt patterns
• Contribute to evaluation suites and regression testing to ensure quality and safety
• Implement guardrails and security controls under guidance from senior engineers
• Instrument services with metrics, traces, logs, and dashboards
• Support performance and cost optimization efforts

Senior Machine Learning Engineer – Generative AI, Full-Stack Applications

• Design, deploy, fine-tune, and evaluate large language models (LLMs); develop effective prompting strategies to optimize performance for various business use cases.
• Improve data quality by cleaning the company’s data and sourcing information from new data sources.
• Use machine learning/ deep learning to enhance and automate processes in Lessen proprietary tools.
• Translate business problems into scalable AI/ML solutions, particularly using LLMs.
• Collaborate cross-functionally with teams to develop, refine, and scale data-driven procedures and workflows.
• Foster a positive team environment.
• Ensure confidentiality and integrity of internal and external data.
• Perform ad-hoc projects and other duties as assigned.

Machine Learning Engineer II

• Design and build agent harness infrastructure: the scaffolding that wraps LLM calls, manages tool use, handles retries, enforces policy, and feeds results back into iterative improvement loops.
• Implement agentic loop patterns with multi-turn reasoning, tool orchestration, memory management, and structured output handling as reusable platform primitives
• Build the agent flywheel: automated pipelines that collect agent traces, surface regressions, route failures to evaluation, and close the loop from production signal back to prompt/model improvement
• Own the end-to-end lifecycle from agent experiment to production deployment, including versioning, rollout controls, and rollback mechanisms
• Build sandboxed execution environments for agent tools with isolating code execution, API calls, and file system access so agents can act without unconstrained blast radius
• Design tiered autonomy models: define which actions agents can take automatically, which require human approval, and which are off-limits and enforced at the infrastructure layer
• Implement replay and dry-run capabilities so new agent versions can be tested against real traces before going live
• Implement evaluation frameworks for agent behavior using a combination of vendor, open source or in house built tools — covering task success, tool selection accuracy, trajectory evaluation, hallucination rates, latency, and cost
• Build and maintain eval datasets, golden trace libraries, and regression test suites that run automatically on every agent code change
• Instrument agent traces end-to-end: LLM calls, tool invocations, intermediate reasoning, final outputs — surfaced in Grafana or equivalent observability tooling
• Define and track agent quality metrics over time; own the signal that tells the team whether agents are getting better or worse
• Drive continuous quality, latency, and cost improvements across deployed agents by closing the loop between production traces, evaluations, and agent design. Optimization may be done through a variety of techniques e.g. prompt tuning, tool calling optimizations, context engineering, right-sizing model selection per task and explore distillation or fine-tuning (SFT, DPO, RLHF) on curated trace data to name a few
• Validate every optimization through A/B tests, shadow deployments, and replay against golden traces, with the eval suite gating rollout so wins are real and regressions are caught before they reach users
• Build and optimize CI/CD pipelines (GitHub Actions, ArgoCD) that cover not just code deployment but agent evaluation gates — no agent ships without passing its eval suite
• Automate Docker and package builds, security scanning, and agent integration tests as first-class pipeline steps
• Design self-healing CI patterns where agent-based automation can diagnose and fix common pipeline failures
• Build internal tools and developer self-service interfaces that let ML engineers and data scientists iterate on agents without platform team involvement
• Maintain a comprehensive view of how all platform components -> infrastructure, agent harnesses, evaluation pipelines, observability — work together
• Create architecture diagrams and drive long-term platform vision; own the "how does this scale to 10x" conversation
• Establish alerting (Grafana, PagerDuty) for both traditional platform health and agent-specific signals (error rates, tool call failures, eval score drift)
• Ensure all agent infrastructure adheres to security best practices: sandboxed execution, auditable traces, access controls on every tool
• Participate in security reviews; own compliance for agent workloads

Principal AI Engineer

• Partner closely with Marketing strategy, operations, and creative leads to surface highest-impact, repeatable workflows
• Design comprehensive end-to-end workflow architectures
• Architect and build scalable, reliable AI agents that execute full marketing workflows
• Design and implement distributed systems that securely integrate with Marketing’s existing tools
• Define and instrument success metrics for every agent
• Maintain and evolve a shared system context repository
• Communicate technical trade-offs and architectural decisions clearly to non-technical Marketing leadership

• Turn quantitative prototypes into reusable tools, services, packaged desktop applications, interfaces, and workflow products that can move from internal demo to client pilot to scaled offer.
• Ship across both cloud-hosted services and locally distributed desktop applications, including Electron-based apps when the workflow or client environment calls for it.
• Build enterprise hardening into the productization layer, including authentication, role-based access control (RBAC), observability, security, release quality, cost controls, and deployment discipline.
• Build evaluation, regression, and release discipline into the productization layer so model logic and agent behavior remain measurable as systems change.
• Work closely with the quant lead so model logic, evaluation intent, and governance requirements survive the move into production.
• Make pragmatic architecture choices across large language models (LLMs), deterministic rules, and hybrid systems based on value, latency, cost, and reliability.
• Help shape repeatable build patterns so strong prototypes become faster, more reliable, and more reusable over time.

See all jobs on JobTailor

AI Engineer

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Staff AI Engineer

AI Engineer, AI Analyst

AI/ML Engineer – Senior Associate

GenAI Engineer – Associate

VP II, AI, Machine Learning

Translational Scientist, Applied Machine Learning, Agentic AI

AI Engineer / AI Analyst

Senior Machine Learning Engineer II, Search & Recommendations Ranking

AI Engineer

Machine Learning Engineer, Conversion ML

Senior AI Engineer

Dynamics 365 Enterprise AI Architect

AI Engineer

Senior Machine Learning Engineer – Generative AI, Full-Stack Applications

Machine Learning Engineer II

Principal AI Engineer

Principal AI Engineer

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