AI Engineer

• Manage mission-critical collaboration and content management systems
• Integrate AI capabilities such as intelligent search, workflow automation, and context-aware assistance
• Collaborate closely with stakeholders, system owners, security teams, and end users
• Stay current with Department AI initiatives and systems
• Design, develop, test, implement, and deploy SharePoint infrastructure and solution components
• Drive innovation within the M365 and SharePoint environments
• Conduct stakeholder consultations and workshops

M365 Automation and AI Developer

• Design, build, and deploy AI-powered tools and automations for HR and Talent use cases, including intelligent workflow automations, employee-facing chatbots, document processing solutions, and decision-support systems
• Identify opportunities to improve manual or repetitive HR workflows through scalable AI/ML-driven automation solutions
• Develop integrations between AI solutions and enterprise systems including Workday, ATS platforms, ServiceNow Ticketing, and other internal tools ensuring seamless data flow and user experience
• Partner directly with HR Operations and cross-functional stakeholders to gather requirements, prototype solutions, run pilots, and drive successful adoption
• Continuously monitor and improve deployed tools based on user feedback, operational metrics, and evolving business needs
• Create technical documentation, implementation guides, and operational handoff materials to support long-term maintainability and scalability.

Principal AI Engineer, HR Automation

• Design and build agentic AI workflows that can plan and execute complex, multi-step tasks
• Build and maintain RAG solutions end-to-end
• Implement retrieval evaluation and continuous improvement loops
• Integrate tools and data sources using MCP or equivalent standard interfaces
• Deploy and operate AI services on AWS
• Engineer robust runtime behavior
• Enable workflow automation and integration
• Create and maintain technical documentation
• Collaborate cross-functionally with product/business stakeholders
• Other duties as assigned

• Design and build agentic AI workflows that can plan and execute complex, multi-step tasks
• Build and maintain RAG solutions end-to-end
• Implement retrieval evaluation and continuous improvement loops
• Integrate tools and data sources using MCP or equivalent standard interfaces
• Deploy and operate AI services on AWS with strong operational practices
• Engineer robust runtime behavior including error handling, retries, fallbacks, circuit breakers, and guardrails
• Enable workflow automation and integration by identifying high-value repetitive processes
• Create and maintain technical documentation for architectures, interfaces, evaluation approaches, and operational runbooks
• Collaborate cross-functionally with product/business stakeholders to validate outcomes against measurable success criteria
• Other duties as assigned.

• Design and implement agentic AI systems capable of multi‑step reasoning, tool use, and autonomous task execution.
• Architect enterprise‑grade GenAI solutions, including RAG pipelines, embedding strategies, orchestration layers, and evaluation frameworks.
• Lead PoCs and pilots and scale successful solutions into production‑ready capabilities.
• Establish reusable architectural patterns, reference implementations, and technical standards for GenAI and agentic systems.
• Integrate AI solutions with existing platforms, data sources, and workflows to support adoption.
• Mentor team developers and contribute to raising overall GenAI and agentic AI maturity.
• Embed governance, security, evaluation, and monitoring considerations into solution design and deployment.

PharmSci Agentic and Generative AI Architect

• Design, develop, and maintain robust, scalable, and high-performance object oriented code in our backend services.
• Develop public REST APIs using Java and internal gRPC APIs for inter-service and inter-system communication.
• Craft systems designs, lead design decisions, and drive alignment with other senior engineers.
• Write automated unit tests, integration tests, end-to-end tests, concurrency tests, load/performance tests.
• Analyze existing systems to identify bottlenecks, tech debt, and implement scalability, and stability improvements.
• Implement automation for testing, monitoring, healing, and scaling applications, continuous integration and deployment to reduce time to market.
• Collaborate with cross-functional teams, including product managers, designers, and other engineers, to define and implement new features.
• Conduct code reviews (comment, approve, seek revisions, merge), mentor junior and mid-level engineers, and actively promote engineering best practices.
• Dive deep and troubleshoot complex issues, devise fixes, author root cause analysis documents, and ensure lasting performance and reliability.
• Conduct objective and comparative analyses of competing technologies to advise the team of pros and cons of a technology solution.
• Maintain robust documentation (design docs, run books, change management docs, and readiness plans).
• Provide live-site support for production applications by monitoring systems, ensuring rapid incident resolution, and driving continuous improvement.
• Drive cross-team projects as a single-threaded-owner (STO) or tech lead, and actively unblock other engineers to make progress.
• Design and build agentic AI systems and services, enabling autonomous workflows, reasoning, and task execution within Mobility platforms.
• Develop AI agents from scratch, including orchestration, tool usage, memory, and multi-step decision-making capabilities.
• Implement and scale multi-agent architectures to support complex, distributed use cases across payments and fleet ecosystems.
• Integrate systems using Model Context Protocol (MCP) or similar frameworks to enable secure and scalable interaction between AI agents, APIs, and enterprise data sources.
• Build and optimize LLM-powered services (e.g., OpenAI APIs, LangChain) for production-grade performance, reliability, and cost efficiency.

Software Engineer 2 – AI Platform

• 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.

QuantAI Engineer – Trading, Investment & Optimization

• Design, develop, and deploy AI-powered solutions including custom GPTs, copilots, and agentic workflows using enterprise LLM platforms and APIs.
• Build Retrieval-Augmented Generation (RAG) pipelines, vector search capabilities, and secure data connectors to enable Verathon-owned data usage.
• Collaborate with AI Business Partners and other departmental representatives to translate functional requirements and use-case concepts into technical implementations.
• Translate business-defined context into structured prompts, system instructions, agent behaviors, and RAG retrieval logic (“technical context engineering”).
• Refine and optimize context injection strategies (prompt templates, chains, tools, memory, retrieval parameters) to improve solution reliability and accuracy.
• Prototype rapidly, iterate based on user feedback, and deliver production-ready AI solutions with appropriate guardrails.
• Develop reusable prompts, templates, automations, and components to accelerate future AI solution development.
• Integrate AI tools with Verathon systems (e.g., Salesforce, Epicor, MasterControl, HRIS) following architecture, security, and compliance standards.
• Partner with the AI Solutions Architect to evaluate and implement new AI tools and ensure alignment with Verathon’s AI Policy and Roper guidelines.
• Document solutions clearly and support functional teams through adoption, training, and scaling of deployed AI tools.

• Define and drive the vision, strategy, and roadmap for enterprise semantic data and data connectivity across business domains
• Partner with business and IT leaders to identify high-value opportunities to connect and standardize data across systems
• Position the semantic data product as a product, not just a technical asset, with clear customers, outcomes, adoption goals, and success metrics
• Define and evolve the product’s value proposition, ensuring alignment to business priorities and measurable outcomes
• Act as the general manager of the product, balancing user needs, technical feasibility, strategic priorities, operational constraints, and business impact
• Define and manage data products aligned to key business domains, including clear ownership, users, and success metrics
• Define target users and use cases for data products, ensuring alignment to specific decisions, workflows, and business outcomes
• Partner closely with business customers to ensure data products support critical decisions, workflows, and operational outcomes
• Drive adoption and usage of shared data assets across business units
• Identify and prioritize use cases where connected data unlocks measurable business value (e.g., improved decisioning, automation, personalization)
• Ensure solutions are aligned to real-world workflows and deliver tangible outcomes
• Lead the development and evolution of enterprise ontologies, taxonomies, and data models that represent key business concepts and relationships
• Establish a scalable semantic layer that enables reuse across analytics, AI, and operational use cases
• Ensure alignment of definitions across domains to reduce fragmentation and duplication
• Support both structured and unstructured data integration, enabling downstream AI and analytics applications
• Ensure data is discoverable, understandable, and usable across teams through consistent definitions and relationships
• Partner deeply with business domains to understand workflows, decision points, and data dependencies, acting as a product owner for how data supports those functions
• Own the end-to-end product lifecycle from discovery and definition to delivery and iteration
• Define product requirements, success metrics, and release plans
• Continuously refine the semantic data product based on user feedback and evolving business needs
• Define critical metrics to measure adoption, usability, and impact of the semantic data product
• Supervise usage and continuously improve accessibility and value delivery
• Evangelize the value of connected semantic data across the organization
• Partner with data governance teams to establish standards, definitions, and data quality expectations
• Promote consistency, reusability, and scalability of data assets across the enterprise

Senior Manager, AI Lead – Semantic Layer

• Collaborate with software and hardware teams to identify high-impact opportunities for applying agentic AI technologies.
• Lead the design, development, and optimization of agentic AI solutions that address challenges in performance, quality and productivity.
• Develop and maintain agentic AI benchmarks to evaluate performance across diverse use cases.

Senior AI Architect, Computer Use Agents

• Design, build, and maintain data pipelines that feed AI Accelerator projects, including ETL workflows, data transformations, and integration with internal and external APIs and data sources.
• Develop and manage relational databases (PostgreSQL/RDS), vector databases (OpenSearch, Milvus, Amazon S3 Vectors), and knowledge graphs (Amazon Neptune, Neo4j).
• Build and operate data infrastructure on AWS, leveraging services such as S3, Athena, RDS, ElastiCache (Redis), and Fargate.
• Build and maintain multi-agent systems using LangGraph, including state management, fan-out/fan-in orchestration, and subgraph composition.
• Develop tool integrations and MCP (Model Context Protocol) servers using FastMCP to enable agent interoperability.
• Implement LLM-powered workflows integrating foundational models (OpenAI, Anthropic, Gemini) via AWS Bedrock and direct API access.
• Build evaluation and observability pipelines using tools such as LangSmith or promptfoo.
• Write and maintain automated test suites (unit, integration, end-to-end) using pytest to ensure reliability of data pipelines and agent workflows.
• Develop evaluation frameworks for LLM outputs, including reference-based metrics and structured output validation.
• Implement data quality checks and validation at system boundaries to catch issues early in the pipeline.
• Support test-driven development practices and contribute to CI test automation.
• Partner with AI Engineers, Pod Leads, and Research scientists to deliver AI Accelerator projects within 12-week cycles.
• Participate in code reviews, design discussions, and technical problem-solving with pod teams.

• Design and implement AI solutions that operate across multi-cloud and hybrid environments, including Azure, Google Cloud Platform (GCP), and AWS.
• Evaluate and recommend hosting, infrastructure, and deployment architectures based on mission requirements, security posture, scalability, portability, and long-term maintainability — not tied to a single cloud provider.
• Architect AI/ML platforms and applications using cloud-native and containerized technologies that minimize vendor lock-in and maximize interoperability.
• Build and modernize web applications using modern full-stack frameworks and AI assisted development tooling to accelerate delivery and improve developer productivity.
• Develop sophisticated conversational AI and knowledge management solutions that enable users to securely interact with enterprise data using natural language.
• Design and implement Retrieval-Augmented Generation (RAG) architectures using vector databases, semantic search, knowledge graphs, and document processing pipelines.
• Lead the design of secure cloud and on-premises AI infrastructure solutions that meet Federal Government and internal compliance requirements.
• Define architectural design decisions affecting infrastructure selection, orchestration, model hosting, observability, scalability, and system behavior for AI enabled solutions.
• Analyze client requirements and emerging AI technology trends to recommend scalable, compliant, and future-proof architectures.
• Participate in design thinking and solution engineering activities to solve complex mission and operational challenges.
• Lead whiteboarding and technical strategy sessions with clients, partners, and internal teams.
• Communicate findings and recommendations to both technical and non-technical stakeholders, translating complex AI and infrastructure concepts into actionable guidance.
• Ensure infrastructure and application architectures support secure software delivery practices including CI/CD, Infrastructure as Code (IaC), automated testing, and model lifecycle management.

• Define architecture patterns and technical standards for AI-enabled applications, copilots, intelligent workflows, automation agents, enterprise knowledge solutions, and reusable AI components.
• Translate business and technology use cases into scalable solution architectures, including application design, data flows, integration patterns, model usage, security controls, and operational requirements.
• Partner with the Sr. Director, AI Platform and Engineering to shape platform architecture, technical roadmaps, reference implementations, and engineering playbooks.
• Provide hands-on architecture leadership in design reviews, technical decision-making, proof-of-concept evaluation, implementation planning, and production readiness.
• Stay current on emerging AI engineering patterns, GenAI platforms, agent frameworks, model orchestration, cloud AI services, enterprise knowledge systems, and secure deployment practices.
• Design reusable platform patterns for model access, retrieval-augmented generation, vector databases, semantic search, embeddings, enterprise knowledge integration, prompt and response handling, and AI observability.
• Create architecture blueprints, technical standards, reusable components, templates, and implementation guidance that improve speed, consistency, quality, and reuse.

Principal AI Engineering Architect

• Define and lead the technical roadmap for TMG’s enterprise AI platform and AI engineering capabilities, aligned to business priorities, enterprise architecture, security standards, and governance expectations.
• Translate AI-First IT strategy into practical platform capabilities, reference architectures, engineering standards, reusable components, and delivery patterns.
• Evaluate AI platforms, cloud services, frameworks, vendor solutions, integration patterns, and development tools with a focus on security, reuse, interoperability, scalability, maintainability, and business value.
• Provide hands-on technical leadership in architecture reviews, solution design, technical decision-making, delivery planning, and complex problem-solving.
• Stay current on emerging AI engineering patterns, GenAI platforms, agent frameworks, model orchestration, enterprise knowledge systems, and responsible deployment practices.
• Partner with business and technology teams to design and deliver AI-enabled capabilities for underwriting, claims, operations, finance, customer service, and other enterprise functions.
• Establish repeatable technical patterns for moving AI use cases from proof of concept to secure, production-ready adoption.
• Build reusable accelerators and implementation playbooks that allow similar AI capabilities to be deployed across multiple business processes with less rework.
• Define architecture patterns for AI-enabled applications, copilots, intelligent workflows, automation agents, enterprise knowledge solutions, and reusable AI components.
• Establish technical standards for model access, prompt and response handling, context management, retrieval-augmented generation, semantic search, vector databases, observability, cost management, and production support.
• Lead delivery of foundational AI platform capabilities such as model gateways, model catalogs, orchestration layers, RAG frameworks, vector stores, embedding pipelines, evaluation frameworks, usage monitoring, and governance controls.
• Establish patterns for integrating AI capabilities with enterprise systems, APIs, data platforms, document repositories, workflow tools, service management platforms, and business applications.
• Create reusable engineering assets, templates, reference implementations, and deployment playbooks that improve delivery speed, quality, consistency, and reuse.
• Guide implementation of Generative AI solutions using LLMs, SLMs, embeddings, prompt engineering, RAG, semantic search, summarization, classification, extraction, and enterprise knowledge retrieval.
• Collaborate with Infrastructure and IT Operations teams to apply AI to observability, incident response, root cause analysis, predictive monitoring, runbook automation, service management, and operational productivity.

Senior Director, AI Platform and Engineering

• Design, develop, and prototype AI/ML solutions addressing Oncology discovery, translational, and clinical development challenges
• Apply advanced analytical and machine learning methods to multimodal datasets (e.g., molecular, clinical, real-world, literature)
• Own solutions end-to-end, from problem framing and data exploration through model development and user facing outputs
• Collaborate closely with domain experts to ensure solutions are scientifically grounded and decision relevant
• Rapidly iterate on prototypes based on user feedback and evolving scientific needs
• Contribute technical expertise to solution design discussions led by the Oncology AI Product & Engineering Lead
• Document methods, assumptions, and limitations to support transparency and responsible AI practices.

Senior Manager, Scientific AI Engineer

• Lead the data engineering team (3 engineers, 2 analysts); conduct performance reviews, provide mentorship, and set career development plans to drive growth
• Work cross-functionally with CS, Sales, Marketing, and Finance to identify needs and construct technical roadmaps for data initiatives
• Establish and enforce enterprise-wide governance frameworks that ensure data privacy, AI safety, and regulatory compliance while standardizing best practices across the data ecosystem
• Oversee the design and deployment of a unified and scalable data architecture and pipelines that support analytics, BI, and machine learning initiatives
• Evaluate business friction points to determine if a BI dashboard, AI agent, or automated workflow is the most effective solution
• Define and track ROI via dashboards for "Hours Saved," "Cost Avoidance," and "Revenue Impact" from AI and data deployments

Director of Data, AI Engineering

• Designing, developing, and deploying generative AI models (e.g., LLMs, diffusion models)
• Architecting and building agentic systems with autonomous decision-making capabilities
• Integrating generative AI and agentic solutions into existing products and services
• Developing and maintaining the software infrastructure required for AI/ML pipeline
• Developing and implementing data pipelines for training and evaluating AI models
• Conducting research and experimentation with new generative AI and agentic techniques
• Other duties as assigned or requested

• Drive the planning, tracking, and execution of engineering projects for AI automation initiatives, ensuring timely delivery of features and integrations (e.g., with Webex, Outlook, SharePoint, Jira, MCP)
• Break down complex product requirements (from PRDs) into actionable engineering tasks and deliverables
• Serve as the primary point of contact between engineering, product, security, and UX teams to resolve dependencies and remove blockers
• Facilitate effective communication and alignment across global, distributed teams
• Identify project risks and issues early; develop and execute mitigation plans in collaboration with engineering leads
• Support implementation of privacy, data retention, and security best practices, ensuring features like persistent memory and data access align with Cisco’s standards
• Recommend and implement improvements to engineering and program management processes
• Advocate for agile practices and tools that drive better collaboration and delivery outcomes

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