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About This Role
Company Description About AbbVie
AbbVie's mission is to discover and deliver innovative medicines and solutions that solve serious health issues today and address the medical challenges of tomorrow. We strive to have a remarkable impact on people's lives across several key therapeutic areas including immunology, oncology and neuroscience \- and products and services in our Allergan Aesthetics portfolio. For more information about AbbVie, please visit us at www.abbvie.com. Follow @abbvie on LinkedIn, Facebook, Instagram, X and YouTube.
Job Description
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 AbbVie’s 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 (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 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 Company's sole and absolute discretion unless and until paid and may be modified at the Company’s 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.
US \& Puerto Rico only \- to learn more, visit https://www.abbvie.com/join\-us/equal\-employment\-opportunity\-employer.html
US \& Puerto Rico applicants seeking a reasonable accommodation, click here to learn more:
https://www.abbvie.com/join\-us/reasonable\-accommodations.html
Salary Context
This $96K-$183K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 2130 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At AbbVie, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $185,000 based on 13,200 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($140K) sits 24% below the category median. Disclosed range: $96K to $183K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
AbbVie AI Hiring
AbbVie has 8 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, Research Scientist. Positions span North Chicago, IL, US, Worcester, MA, US, Florham Park, NJ, US. Compensation range: $125K - $305K.
Location Context
AI roles in Chicago pay a median of $200,100 across 329 tracked positions.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (1,901) and senior (1,663) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $274,200 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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