Senior Engineer, Applied AI & Engineering Platforms

Agentic System Design & Engineering

• Architect and own production-grade multi-agent systems using orchestration frameworks (LangChain, LangGraph, CrewAI, OpenAI Agents SDK, AutoGen/AG2, Semantic Kernel), making deliberate decisions on state management, routing, memory architecture, and failure handling.
• Design agent cognitive architectures planning loops (ReAct, Reflexion, CoT), tool-use patterns, memory systems (short-term, episodic, semantic), and self-evaluation loops.
• Build multi-agent coordination patterns (supervisor-worker, peer collaboration, A2A protocols) aligned with emerging open standards including MCP server integration to connect agents to enterprise systems, clinical data platforms, and regulatory repositories.

AI Foundations Layer

• Design and maintain shared AI infrastructure: LLM gateway/routing, embedding services, vector stores, RAG pipelines, prompt management, and model evaluation harnesses across all agentic products.
• Establish model selection and governance spanning hosted providers (Claude, GPT, Gemini) and self-hosted models, including fine-tuning pipelines (LoRA/QLoRA) for pharmaceutical-specific tasks.
• Build context engineering standards managing context windows, retrieval strategies, chunking, re-ranking, hybrid search, and query routing for enterprise-scale clinical and scientific knowledge with guardrails, safety layers, content filters, and HITL escalation appropriate for GxP environments.

Agentic Engineering SDLC

• Define the end-to-end SDLC for agentic systems from design through evaluation, deployment, and continuous monitoring treating agent behavior as a first-class software artifact subject to change control.
• Build agent evaluation frameworks (golden test sets, LLM-as-judge scoring, regression detection, task-completion benchmarks, latency/cost dashboards) and CI/CD pipelines with automated evaluation gates, drift detection, and rollback capabilities.
• Establish traceability, audit logging, and versioning standards supporting GxP validation, 21 CFR Part 11, and AbbVie’s AI governance policy.

Observability, Reliability & AIOps

• Implement full-stack observability (LangSmith, Langfuse, OpenTelemetry): trace-level logging, token/cost tracking, latency profiling, and anomaly detection on agent behavior.
• Own production reliability retry logic, fallback strategies, circuit breakers, graceful degradation, and HITL escalation for regulated workflows. Monitor for behavior drift and decision inconsistency; implement continuous feedback loops without introducing regressions.
• Integrate agentic services with enterprise platforms (Salesforce, MuleSoft, Veeva, SAP, Databricks, ServiceNow) using MCP and standardized API patterns.

Governance, Compliance & Responsible AI

• Design agent authorization models operationalizing AbbVie’s AI risk tiers (HIGH/LOW), defining what agents can access, act on, and decide autonomously versus what requires human approval.
• Implement governance controls aligned with FDA AI/ML guidance, ICH E6/E8, EU AI Act, and AbbVie internal policy ensuring compliance with data residency, privacy (HIPAA, GDPR), least-privilege access, prompt injection defense, and secure MCP/A2A integrations.
• Build validation artifacts satisfying audit requirements for agents in clinical, regulatory, and GxP-controlled workflows.

Cross-Functional Technical Leadership

• Partner with product managers, data scientists, enterprise architects, platform security, and domain teams to translate pharmaceutical problems into agent system designs; define reusable patterns and shared platform components that accelerate development across teams.
• Mentor engineers on the agentic AI platform, conduct architecture reviews, establish engineering standards, and foster a culture of production-quality AI development while driving adoption of emerging standards (MCP, A2A, evaluation benchmarks) relevant to AbbVie’s environment.

Qualifications

• Minimum years of experience: 6+ with Bachelors, or 5+ with Masters, or 0+ with PhD in software engineering with demonstrated depth in AI/ML systems, NLP/LLM applications, or production AI platforms including experience building Generative AI or LLM-powered applications in production environments.
• Strong Python proficiency including async programming (asyncio), RESTful API design (FastAPI), system design patterns for scalable distributed AI systems, and production-quality coding practices.
• Hands-on experience building and operating RAG pipelines: embedding models, vector databases (e.g., pgvector, Pinecone, Azure AI Search), chunking strategies, hybrid retrieval, and retrieval evaluation. Familiarity with LlamaIndex or similar RAG frameworks is a plus.
• Experience with one or more cloud AI platforms (AWS Bedrock, Azure AI Foundry, or Google Vertex AI) including serverless inference and managed agent services.
• Solid understanding of prompt engineering at the system level: system prompt design, structured output formats, tool-call schemas, context engineering, and prompt versioning.
• Clear communication skills ability to articulate agent architecture decisions, risk tradeoffs, and compliance implications to both technical engineers and non-technical business stakeholders.