AI Engineer - GenAI

CVS Health•Farmers Branch, TX

16h•$72,100 - $144,200•Hybrid

About The Position

CVS Health is seeking an AI Engineer to design, build, and deploy generative AI and agentic solutions for enterprise business needs. Working as part of an engineering team under the guidance of senior and staff engineers, this hands-on role develops agentic frameworks, data pipelines, retrieval-augmented generation (RAG), and agent-based applications. The engineer develops defined features and components from conception through production support while building expertise in machine learning engineering, cloud platforms, and responsible AI practices. The position is onsite two days per week at the CVS office in Farmers Branch, Texas, or Scottsdale, Arizona. It is a full-time role with 40 anticipated weekly hours.

Requirements

At least 2 years of hands-on experience developing and deploying machine learning or AI applications.
At least 2 years of Python experience, including building production-quality code and APIs such as FastAPI or Flask.
At least 2 years of SQL experience and experience with relational and/or NoSQL databases.
At least 2 years of hands-on experience with a deep learning framework such as PyTorch, TensorFlow, or Keras.
At least 2 years of experience with core AI/ML concepts, including model training, validation, evaluation metrics, and feature engineering.
At least 1 year of hands-on experience building or contributing to GenAI solutions, including RAG and LLM integration such as prompt design, tool calling, and embeddings.
At least 1 year of experience with a vector database such as Pinecone or Qdrant, or comparable search/retrieval technology.
At least 1 year of experience working on a cloud platform (GCP or Azure preferred; AWS acceptable), including storage, compute, and basic IAM concepts.
At least 1 year of experience with Git and collaborative development workflows such as pull requests and code reviews.
At least 1 year of experience working in an Agile team environment.
At least 1 year of experience communicating technical concepts clearly and working effectively with cross-functional partners.
Bachelor's degree or equivalent experience (high school diploma plus 4 years of relevant work experience).

Nice To Haves

Master's degree; strongly preferred.
Experience with multi-agent frameworks such as LangGraph or LangChain and agent memory patterns.
Experience with graph-based retrieval (GraphRAG) or HydraDB.
Experience with workflow automation tools.
Experience with NLP techniques such as text classification, entity extraction, embeddings, and semantic search.
Experience with distributed data processing using PySpark and analytics on BigQuery.
Experience with Elasticsearch, MongoDB (Atlas preferred), or PostgreSQL beyond basic usage.
Experience with Docker and containerized deployments; exposure to Kubernetes (GKE) or Cloud Run.
Exposure to CI/CD tools such as GitHub Actions, Jenkins, or Argo.
Working knowledge of Java, C++, or R for specific modeling, integration, or performance needs.
Familiarity with responsible AI practices, including data privacy, bias awareness, and handling sensitive data in regulated environments.
Experience using AI-assisted development tools such as Claude, OpenAI, or Gemini for code analysis, refactoring, or agentic workflows.

Responsibilities

Build GenAI and agentic AI components, including RAG pipelines, tool-calling agents, agent memory, and multi-agent workflows using vector databases and graph retrieval patterns.
Develop and maintain data pipelines for ingestion, transformation, and feature preparation using Python, SQL, and PySpark across BigQuery, PostgreSQL, MongoDB, and Elasticsearch.
Implement computer vision solutions, including object detection and tracking using OpenCV and Roboflow, along with dataset preparation and annotation workflows.
Apply statistical and unsupervised techniques for analytics, segmentation, and causal-impact use cases.
Build workflow automations and integrations on cloud services.
Write clean, tested, well-documented code; participate in code reviews; and follow established CI/CD, security, and compliance standards.
Monitor production model and agent performance and help troubleshoot data drift, retrieval quality, latency, and failures.
Support evaluation of model and agent quality using defined metrics, test sets, and human-in-the-loop review.
Collaborate with product owners, data engineers, and senior engineers to translate requirements into technical tasks and communicate progress, blockers, and risks.
Contribute to team knowledge-sharing and stay current with emerging AI/ML tools and techniques, including AI-assisted development.