Develop AI‑powered applications using state‑of‑the‑art LLMs
Build and deploy applications for internal customers, selecting the right models (e.g., GPT‑4o, Claude Sonnet 4, Gemini 2.5 or open‑source alternatives)
Implement retrieval‑augmented generation (RAG) pipelines and agents using LangChain, LangGraph, LlamaIndex, and other emerging frameworks
Integrate ML models with front-end apps, REST APIs, and backend systems
Design and build end-to-end AI systems—ranging from chatbots and recommendation engines to vision-based pipelines
Build robust logic around ML models to address edge cases, failovers, and complex business rules
Leverage prebuilt models or services (e.g., OpenAI API, Azure Cognitive Services, HuggingFace) to accelerate development cycles
Fine‑tune and customize models using LoRA, prompt tuning, or RLHF techniques
Occasionally perform custom model training—but more often fine-tune or adapt existing models to meet business needs
Work with vector databases (e.g., Pinecone, Weaviate) and explore graph-based approaches (e.g., GraphRAG, Knowledge Graphs)
Ensure privacy and compliance with HIPAA, including encryption, PHI protection, and vendor due diligence
Prototype multi‑agent systems using frameworks like CrewAI or Stands SDK and evaluate emerging agent orchestration solutions
Collaborate cross-functionally with product managers, data scientists, and compliance teams
Mentor junior engineers, lead innovation discussions, and continuously evaluate emerging LLM architectures

Requirements

Bachelor’s degree from an accredited college in Computer Science, Computer Engineering, Statistics, Data Science or another similar quantitative field
9+ years in software engineering or machine learning, with 2+ years building production AI/ML systems
Strong programming skills in Python and Typescript
Experience with REST APIs and frameworks like Flask or FastAPI
Familiarity with LangChain, LlamaIndex, HuggingFace Transformers, SGLang, and OpenAI API
Experience with RAG architectures, including vector databases and GraphRAG approaches
Experience deploying AI solutions using cloud platforms (AWS, Azure, or GCP)
Understanding of HIPAA compliance: handling PHI, encryption protocols, and vendor BAAs
Experience in prompt engineering, prompt security, and evaluation strategies
Familiarity with containerization, CI/CD, and MLOps pipelines

Benefits

Competitive compensation
Health and Wellness programs
Income Protection
Paid Leave
Retirement

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

PythonTypescriptREST APIsLangChainLlamaIndexHuggingFace TransformersRAG architecturesprompt engineeringMLOpscontainerization

Soft Skills

mentoringcollaborationinnovation leadership

Certifications

Bachelor’s degree in Computer ScienceBachelor’s degree in Computer EngineeringBachelor’s degree in StatisticsBachelor’s degree in Data Science