Machine Learning Engineer

Key Responsibilities

• Design, implement, and optimize AI/ML models particularly leveraging LLMs, RAG, and prompt engineering for production grade applications.
• Develop and orchestrate multi-agent systems using frameworks such as LangGraph and LangChain.
• Integrate and deploy solutions on secure cloud environments, including AWS GovCloud, Google GovCloud, Azure IL5+, Vertex AI, and AWS Bedrock.
• Create and maintain REST APIs and SDK integrations to facilitate seamless data and model interactions.
• Collaborate with product, security, and engineering teams to ensure best-in-class delivery, following secure coding and DevOps best practices (CI/CD).
• Document technical decisions and communicate complex concepts clearly to both technical and non-technical stakeholders.

Required Skills and Qualifications

• Proficiency in Python for AI/ML development, including experience with REST APIs and SDK integration.
• Hands-on experience with LLMs, RAG systems, and prompt engineering in production environments.
• Familiarity with multi-agent orchestration, tool use, and frameworks like LangGraph and LangChain.
• Deep understanding of cloud AI services (AWS GovCloud, Google GovCloud, Azure IL5+, Vertex AI, AWS Bedrock).
• Background in building and maintaining data pipelines, ontologies, metadata catalogs, and ETL processes.
• Strong grasp of secure coding and modern DevOps practices (CI/CD pipelines).
• Exceptional written and verbal communication skills for effective collaboration and documentation.

Preferred Qualifications

• Experience with AI/agentic platforms, such as Anthropic for Gov, OpenAI Enterprise, Gemini Enterprise, or Grok Enterprise.
• Knowledge of metadata catalog platforms (MCP) and advanced API development techniques.
• Prior exposure to government or highly regulated cloud environments and compliance standards.