Senior Machine Learning Engineer

Job Title: Senior Machine Learning Engineer – LLMs & Cloud AI
Location: Hybrid (Remote + Onsite as needed)
Clearance Requirement: Must be eligible for Public Trust

About the Role
Join a mission-driven team at the forefront of AI innovation in the public sector. We’re seeking a Senior Machine Learning Engineer with deep expertise in Large Language Models (LLMs), LangChain, and OpenAI technologies to lead the development of intelligent, secure, and scalable AI systems in a cloud-native environment.

This role is ideal for a seasoned AI professional ready to architect and deploy advanced ML workflows that support federal initiatives and deliver real-world impact.

Key Responsibilities

• Design, fine-tune, and deploy LLMs (e.g., GPT-4, LLaMA, T5) for generative AI, summarization, classification, and conversational tasks.
• Build intelligent agent-based applications using LangChain, including tools, chains, memory, and RAG pipelines.
• Architect and deploy AI/ML systems on AWS (SageMaker, Lambda, Bedrock, Redshift, S3, ECS/EKS).
• Integrate OpenAI APIs (GPT-4, embeddings, function calling) into secure, compliant federal applications.
• Develop robust MLOps pipelines using MLflow, SageMaker Pipelines, and Step Functions.
• Implement and manage vector databases (FAISS, Pinecone, OpenSearch) for embedding-based search.
• Ensure all implementations meet federal security, privacy, and compliance standards.
• Experiment with prompt engineering, hyperparameter tuning, and inference optimization.
• Collaborate with engineering, DevOps, and compliance teams to deploy production-ready AI services.
• Document architecture and workflows to support transparency and auditability.
• Stay current with emerging trends in LLMs, LangChain, OpenAI, and AWS AI/ML services.

Required Skills and Experience

• Bachelor’s or Master’s degree in Computer Science, Data Science, AI, or a related field.
• 8+ years of experience in machine learning and AI system development.
• Strong background in NLP and transformer-based architectures (GPT-style models).
• Hands-on experience with LangChain and OpenAI APIs.
• Proficiency in AWS services (SageMaker, Bedrock, Lambda, S3, Redshift, ECS/EKS).
• Skilled in Python and libraries such as Hugging Face, PyTorch, TensorFlow, FastAPI, and scikit-learn.
• Solid understanding of MLOps, CI/CD, containerization, and pipeline automation.
• Must be eligible to obtain and retain Public Trust clearance.

Preferred Qualifications

• Experience supporting federal agencies or public sector AI initiatives.
• Expertise in retrieval-augmented generation (RAG), prompt engineering, and embedding-based search.
• Familiarity with LangSmith, OpenAI fine-tuning, and multi-agent systems.
• Experience with Docker, Kubernetes, and scalable containerized services.
• Knowledge of ethical AI, bias mitigation, and AI governance best practices.