Software Development Engineer (AI Engineering)

CVS Health•Richardson, TX

1d•$72,100 - $144,200

About The Position

CVS Health is building a world of health around every individual, shaping a more connected, convenient, and compassionate health experience. The company fosters an environment where colleagues are passionate, innovative, accountable, and prioritize safety and quality. This role is part of a larger mission to simplify health care one person, one family, and one community at a time. The position is for individuals passionate about building real, production-ready AI systems, offering the opportunity to engage in meaningful, hands-on work at the forefront of applied AI engineering. The role involves designing and developing AI-powered applications and features using Python, leveraging LLMs, RAG (Retrieval Augmented Generation), and agentic AI frameworks. This includes building and deploying production-ready AI agents and assistants to automate workflows and enhance user experiences across enterprise applications, implementing RAG pipelines, and integrating AI features into existing applications using RESTful APIs, microservices architecture, and cloud-native patterns. The engineer will apply prompt engineering best practices, collaborate with various teams, evaluate AI models and tools, write clean and well-tested code, monitor AI application performance, and stay current on emerging AI technologies and industry best practices.

Requirements

1-2+ years of software development experience with Python
Experience building applications using LLMs (OpenAI, Anthropic, Azure OpenAI, or open-source models)
Hands-on experience with RAG (Retrieval Augmented Generation) implementations
Familiarity with vector databases
Experience with at least one cloud platform (AWS, Azure, or GCP)
Proficiency in Git version control and collaborative development workflows

Nice To Haves

Experience with Agentic AI frameworks (LangChain Agents, AutoGen, CrewAI, or similar)
Familiarity with AI-assisted development tools and practices (GitHub Copilot, Cursor, or similar)
Experience with containerization (Docker) and orchestration (Kubernetes)
Knowledge of CI/CD pipelines (GitHub Actions, Jenkins, or similar)
Experience with FastAPI, Flask, or similar Python web frameworks
Familiarity with frontend technologies (React, JavaScript) for full-stack AI applications
Experience with prompt engineering, fine-tuning, and model evaluation techniques
Understanding of AI safety, responsible AI practices, and ethical considerations
Experience with document processing libraries (LangChain document loaders, Unstructured, etc.)
Familiarity with observability tools for AI applications (LangSmith, Weights & Biases, or similar)
Strong problem-solving skills and ability to debug complex AI system behaviors
Understanding of RESTful API design and integration

Responsibilities

Designs and develops AI-powered applications and features using Python, leveraging LLMs, RAG (Retrieval Augmented Generation), and agentic AI frameworks.
Builds and deploys production-ready AI agents and assistants that automate workflows and enhance user experiences across enterprise applications.
Implements RAG pipelines including document ingestion, chunking strategies, vector embeddings, and semantic search to enable intelligent information retrieval.
Develops and integrates AI features into existing applications using RESTful APIs, microservices architecture, and cloud-native patterns.
Applies prompt engineering best practices to optimize LLM outputs for accuracy, consistency, and reliability in production environments.
Collaborates with software engineers, product managers, and UX designers to translate business requirements into AI-powered solutions.
Evaluates and selects appropriate AI models, frameworks, and tools based on use case requirements, cost, and performance considerations.
Writes clean, maintainable, and well-tested code following software engineering best practices including code reviews, CI/CD, and version control.
Monitors AI application performance, implements logging and observability, and continuously improves model accuracy and response quality.
Stays current on emerging AI technologies, frameworks (LangChain, LlamaIndex, CrewAI), and industry best practices in AI application development.