AI Engineer
Removed from the employer's board on Nov 6, 2026. First seen by Alion on Nov 2, 2026. AbbVie scores B on the Alion truth index.
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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.
This entry-level AI Engineer will work under close supervision to apply developing technical knowledge in Software Engineering and Generative AI, performing analytic, testing, modeling, and development tasks on smaller projects, and independently on larger, more complex assignments. The role will help design, build, and deploy AI-enabled software solutions, including LLM-powered applications, retrieval-augmented generation (RAG) pipelines, and model-serving APIs, to meet business requirements.
Responsibilities:
- Perform assigned technical and analytic tasks using established methods and tools, documents methods and results, and escalate novel or ambiguous issues.
- Support development of AI-enabled applications and fix straightforward defects under close supervision.
- Build and maintain components of LLM-based solutions, including API integrations, prompt and tool/function-calling workflows, and basic RAG pipelines (chunking, embeddings, vector search).
- Clean, transform, and analyze data with Python, SQL, and data libraries; build simple ETL steps and visualizations.
- Execute assigned test cases, including unit tests and basic retrieval or model evaluations, and help reproduce reported issues.
- Serve models and AI services through RESTAPIs and containers, prepare simple configuration changes, and follow deployment runbooks.
- Track experiments and model versions and support monitoring and CI/CD processes.
- Document work and escalation steps in tickets and knowledge base entries.
- Learn codebase, development workflows, and platform fundamentals through pairing and code reviews.
- Produce reliable technical outputs, follow quality and safety standards (including AI guardrails), document reproducible methods and records, and communicate progress and risks to project leads.
- Bachelor’s Degree in Computer Science, Data Science, Engineering, Mathematics, or relevant STEM certification.
- Problem decomposition, clear communication, reading documentation, collaborating through code review, and explaining results to non-technical people.
- Python and Software Fundamentals
- Core language: data types, control flow, functions, OOP, comprehensions, generators, decorators, exceptions, file I/O, virtual environments (venv, pip / uv)
- Data libraries: NumPy, pandas (cleaning, joins, groupby, time series)
- Visualization: Matplotlib, Seaborn, Plotly
- Code quality: type hints, unit testing (pytest), logging, PEP 8, packaging
- APIs and web: requests, REST concepts, FastAPI or Flask to serve models, async basics
- Tooling: Git/GitHub, Jupyter, VS Code, Docker basics
- Databases
- SQL (essential): SELECT, WHERE, JOINs, GROUP BY, HAVING, subqueries, CTEs, window functions, indexes, query optimization
- Relational databases: PostgreSQL, MySQL, or SQLite; schema design, normalization, primary and foreign keys, transactions
- Python connectivity: SQLAlchemy, psycopg2, pandas.read_sql
- NoSQL: MongoDB (documents), Redis (caching)
- Vector databases (AI-specific): embeddings, similarity search, and tools such as pgvector, Chroma, FAISS, Pinecone
- Data pipelines: ETL basics, data warehouses, Airflow concepts
- Modern GenAI Skills
- LLM APIs: Anthropic, OpenAI, structured outputs, tool/function calling
- RAG: chunking, embeddings, vector search, retrieval evaluation
- Frameworks: LangChain or LlamaIndex (know the basics, but understand what's underneath)
- Agents: tool use, multi-step workflows, basic evaluation, and guardrails
- MLOps and Deployment
- Model serving: FastAPI, Docker
- Experiment tracking: MLflow, Weights & Biases
- Cloud basics: AWS, Azure, or GCP
- CI/CD basics, monitoring, model versioning
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
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https://www.abbvie.com/join-us/reasonable-accommodations.html