AI/ML Engineer

Role Overview

• 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

Tech Stack

• AWS
• Azure
• Cloud
• Flask
• Google Cloud Platform
• Python
• TypeScript

Benefits

• Competitive compensation
• Health and Wellness programs
• Income Protection
• Paid Leave
• Retirement