Senior, Agentic AI Engineer

The Senior AI Agentic Engineer designs builds and operationalizes intelligent agent systems that automate complex enterprise business processes end-to-end. This role works at the intersection of LLMs systems engineering and applied machine learning architecting multi-agent pipelines tool-augmented reasoning systems and retrieval-augmented generation (RAG) workflows across a range of enterprise platforms (e.g. Databricks AgentBricks Azure OpenAI) and open-source frameworks (e.g. LangChain LangGraph AutoGen) with the expectation that the right candidate brings familiarity with the broader and rapidly evolving ecosystem.

The ideal candidate brings deep hands-on engineering experience with a proven track record of delivering agentic AI systems into production at enterprise scale not just prototypes applying rigorous software engineering principles including modular system design testability resilience engineering and security-by-design to ensure agents are maintainable reliable and safe in the long run. This means architecting for failure building in retries fallbacks and graceful degradation and treating latency and cost as first-class engineering constraints from day one not afterthoughts discovered in production. Beyond technical delivery the Senior AI Agentic Engineer mentors engineers across the team shapes the organizations AI automation strategy translates ambiguous business problems into well-structured agentic solutions and drives the responsible and secure deployment of AI agents across business-critical functions.

What You Will Do:

Agentic AI System Design & Development

• Design build and deploy end-to-end agentic AI systems using LLMs tools memory and planning frameworks to automate complex multi-step enterprise business processes.
• Architect and implement both single-agent and multi-agent workflows for autonomous task execution decision support and orchestration defining agent roles memory strategies tool integrations and handoff protocols.
• Develop tool-using agents with function calling structured outputs API integrations database connectors RPA hooks and enterprise workflow triggers.
• Lead the integration of agentic solutions with enterprise systems including ERP Business apps and orchestration platforms such as Databricks Airflow and Azure Data Factory.

Retrieval-Augmented Generation (RAG)

• Design and optimize RAG pipelines including document ingestion chunking strategies embedding models vector store selection and retrieval ranking for enterprise knowledge bases.
• Implement advanced retrieval techniques such as hybrid search metadata filtering re-ranking and query rewriting to improve grounding and reduce hallucination.
• Evaluate and continuously tune RAG systems for accuracy latency factual grounding and cost efficiency.

Model Adaptation & Prompt Engineering

• Evaluate and select frontier and open-source LLMs (e.g. GPT-4o Claude Llama Mistral Gemini) and apply fine-tuning strategies including instruction tuning appropriate to each business use case.
• Optimize prompts system instructions and output schemas for reliability determinism and safety across agentic pipelines.
• Apply reinforcement or feedback-driven optimization where applicable including human-in-the-loop and automated evaluation loops.

Evaluation Monitoring & Governance

• Define evaluation frameworks for agentic systems covering task success factuality grounding latency cost and failure mode analysis.
• Build observability and monitoring pipelines for agent behavior tool call traces and runtime failure detection.
• Partner with governance risk and compliance teams to ensure responsible AI practices audit traceability data privacy and regulatory adherence across all deployed agents.

Production Deployment & LLMOps

• Deploy GenAI and agentic systems into production using cloud-native architectures on platforms such as Azure AWS Bedrock or Google Vertex AI with containerized (Docker/Kubernetes) delivery.
• Implement CI/CD pipelines prompt versioning rollback strategies and runtime safeguards for LLM applications in enterprise environments.
• Optimize deployed systems for performance cost efficiency and scalability under real-world load.

Collaboration Mentorship & Strategy

• Collaborate with software engineers product managers data scientists and business stakeholders to translate ambiguous process challenges into well-structured agentic solutions.
• Mentor AI engineers and data scientists on agentic design patterns responsible AI practices and production-grade engineering standards.
• Contribute to the organizations AI automation strategy co-authoring technical roadmaps governance policies and center-of-excellence standards for agentic AI.
• Stay at the forefront of the agentic AI landscape rapidly evaluating new frameworks and research findings and communicating their business relevance to leadership.

What You Will Need:

Education and Experience

• 3 years work experience as an AI Agentic Engineer and over 7 years experience in Data Science Gen AI Information Systems Computer Science Software Engineering or other relevant field with relevant experience.
• Preferred - Masters Degree in Data Science Data Analytics Information Systems Computer Science Engineering or other relevant field. Required bachelors degree in data science Information Systems Computer Science Engineering or other relevant field with relevant experience.
• Prefer to have Gen AI Certifications

Knowledge Skills and Abilities

Technical Agentic Frameworks & LLMs

• 1. Proven enterprise experience architecting and deploying production-grade multi-agent AI systems that automate real business workflows end-to-end not just proofs of concept.
• 2. Deep hands-on expertise with agent orchestration frameworks such as LangChain LangGraph AutoGen Semantic Kernel DSPy CrewAI and platform-native solutions such as Databricks AgentBricks / Mosaic AI Agent Framework with openness to emerging tools in the rapidly evolving ecosystem.
• 3. Deep understanding of LLMs and foundation models (e.g. GPT Claude Llama Mistral Gemini) including their capabilities limitations and appropriate use case fit.
• 4. Experience with structured outputs function/tool calling JSON schema design and multi-turn agent loop engineering.

Technical RAG & Data Platforms

• 5. Strong knowledge of RAG architectures vector databases (e.g. Pinecone Weaviate Chroma pgvector) embedding models and hybrid retrieval strategies.
• 6. Hands-on experience with Databricks including Unity Catalog MLflow Delta Lake and Databricks Workflows for end-to-end data and AI pipelines.
• 7. Experience with database technologies data lakes and enterprise data platforms including SQL cloud storage and streaming data sources that agents consume at runtime

Technical Deployment MLOps & Engineering

• 8. Strong Python proficiency and experience building production-grade services APIs and microservices that support agentic systems.
• 9. Experience deploying LLM and agent workloads on cloud platforms (e.g. Azure OpenAI Service AWS Bedrock Google Vertex AI) with containerized infrastructure (Docker Kubernetes).
• 10. Experience implementing LLMOps practices including experiment tracking (e.g. MLflow W&B) prompt versioning evaluation harnesses latency profiling and CI/CD for AI systems.
• 11. Experience with enterprise security data governance and compliance requirements for AI deployments including PII handling role-based access control and audit logging.

Evaluation & Responsible AI

• 12. Familiarity with LLM evaluation techniques failure mode analysis red-teaming and benchmark construction to maintain quality and trust in production agents.
• 13. Working knowledge of responsible AI principles including fairness explainability safety guardrails and human oversight mechanisms in agentic deployments.

Leadership & Communication

• 14. Strong written and verbal communication skills with the ability to explain complex GenAI and agentic concepts clearly to both technical teams and executive stakeholders.
• 15. Demonstrated ability to lead cross-functional AI projects from discovery through production aligning engineering data product legal and business operations teams.
• 16. Ability to mentor junior team members establish engineering standards and foster a culture of experimentation and responsible AI development.
• 17. Ability to translate ambiguous open-ended business challenges into structured agentic solution designs with clear scope and success criteria.
• 18. Display an entrepreneurial mindset with a bias for practical high-impact solutions; comfortable operating in ambiguous environments and rapidly evolving technology landscapes.
• 19. Working knowledge of ABS Health Safety Quality and Environmental Management System.

Reporting Relationships:

Reports to the Senior Manager on the Data and Analytics team

This position requires access to information that is subject to control by the Export Administration Regulations and/or the International Traffic in Arms Regulations. Any offer of employment shall be contingent upon the Companys verification that the candidate is a U.S. Person or upon the receipt of all necessary export licenses or authorizations that may be required by U.S. export control laws. U.S. Persons are defined as U.S. citizens U.S. lawful permanent residents (i.e. green card holders) or any individual granted protected status under the Immigration and Nationality Act (8 U.S.C. 1324b(a)(3)) including asylees and the event a candidate refuses or cannot otherwise provide the necessary information for the Company to determine whether such licenses may be required or for the Company to obtain any required licenses the Company shall maintain the exclusive right to discontinue the application process and/or withdraw any contingent offer that has been made.