Senior Engineer, Applied AI & Engineering Platforms

Join an inclusive collaborative Business Technology Solutions (BTS) team as a Sr Engineer Applied AI & Engineering Platforms at AbbVie. This is a hands-on lead technical engineering role at the center of AbbVies generative and agentic AI transformation building intelligent autonomous systems and scalable agentic workflows that will accelerate drug discovery streamline clinical and regulatory operations and reimagine how AbbVie works across every function. 

You will design and own the AI foundations layer that underpins all agentic capabilities across the enterprise establish engineering standards that make AI systems reliable and auditable in GxP-regulated environments and serve as a technical authority guiding platform teams data scientists and application engineers across the organization. 

This is not a research or prototyping role. You will architect build and operate production-grade multi-agent systems used in clinical commercial and operational domains working alongside enterprise architecture platform security data engineering MLOps and domain subject matter experts to ensure every system is deployable governed and compliant from day one. 

Responsibilities:

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 (supervisorworker 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 AbbVies 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 AbbVies 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 AbbVies environment. 

Qualifications :

Required: 

• 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. 
• Demonstrated hands-on experience architecting and deploying production-grade AI agent or multi-agent systems not prototypes or POCs using at least one major orchestration framework (LangChain LangGraph CrewAI OpenAI Agents SDK AutoGen/AG2 or Microsoft Semantic Kernel). 
• 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. 

Preferred: 

• Working proficiency with LLMOps/AIOps tooling (LangSmith Langfuse MLflow or equivalent) for agent observability experiment tracking and production monitoring. 
• Experience designing and implementing agent evaluation frameworks including test dataset design LLM-as-judge scoring regression benchmarking and responsible AI practices. 
• Open-source contributions published work or conference presentations in agentic AI multi-agent systems LLM engineering machine learning or related areas. 
• Strong experience with MCP (Model Context Protocol) A2A (Agent-to-Agent) or equivalent tool-integration and agent communication standards; TypeScript or Go proficiency for MCP server development or full-stack AI delivery. 
• Experience in pharmaceutical life sciences biotech or other regulated industry environments with exposure to GxP 21 CFR Part 11 FDA AI/ML guidance ICH E6/E8 or ISO 42001 standards. 
• Hands-on experience integrating AI agents with enterprise platforms (Salesforce Veeva Vault SAP ServiceNow Databricks MuleSoft) or processing multimodal clinical/scientific data. 
• Background in distributed systems or microservices architecture (event-driven serverless Kubernetes); familiarity with Docker container orchestration PyTorch or Hugging Face for model experimentation. 
• AWS Azure or GCP professional-level certifications; familiarity with AI-assisted development workflows (Cursor AI GitHub Copilot). 

Additional Information :

Applicable only to applicants applying to a position in any location with pay disclosure requirements under state or local law: 

• The compensation range described below is the range of possible base pay compensation that the Company believes in good faith it will pay for this role at  the time of this posting based on the job grade for this position. Individual compensation paid within this range will depend on many factors  including  geographic location and we may ultimately  pay more or less than the posted range. This range may be  modified in the future. 

• We offer a comprehensive package of benefits including paid time off (vacation holidays sick) medical/dental/vision insurance and 401(k) to eligible employees.

• This job is eligible to  participate in our short-term incentive programs. 

Note: No amount of pay is  considered to be wages or compensation until such amount is earned  vested  and determinable. The amount and availability of  any bonus commission incentive benefits or any other form of compensation and benefits that are allocable to a   particular employee  remains in the Companys sole and absolute discretion unless and until paid and may be  modified at the Companys sole and absolute discretion consistent with applicable law. 

AbbVie is an equal opportunity employer and is committed to operating with integrity driving innovation transforming lives and serving our community. Equal Opportunity Employer/Veterans/Disabled. 

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Work :

No

Employment Type :

Full-time