Machine Learning Engineer

Job Title/Role: Machine Learning Engineer

Key Skills: Machine Learning, Agentic AI

Experience: 6- 10+ Years

Location: Richmond, VA (Onsite)

We at Coforge are seeking a Machine Learning Engineer with the following skillset:

Key Responsibilities:

• Design and implement agentic AI systems capable of planning, tool use, memory, and multi-step reasoning.
• Build and deploy AI solutions using Azure AI Foundry and Copilot Studio.
• Develop RAG pipelines integrating structured and unstructured enterprise data.
• Implement and optimize vector databases for semantic search and long-term agent memory.
• Orchestrate LLM-based agents using frameworks such as LangChain (or equivalent).
• Develop scalable backend services and APIs using Python.
• Integrate AI agents with enterprise tools, APIs, and workflows.
• Evaluate, monitor, and optimize agent performance, reliability, and cost.
• Apply responsible AI principles including security, privacy, and governance.
• Stay current with advancements in LLMs, agent architectures, and Azure AI services.

Required Skills & Experience:

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• 5+ years of experience in machine learning, AI engineering, or applied ML.
• Strong proficiency in Python for ML and backend development.
• Hands-on experience building LLM-based applications.
• Practical experience with agentic AI patterns (tool calling, planning, memory, reflection).
• Experience with LangChain or similar agent orchestration frameworks.
• Solid understanding of RAG architectures.
• Experience with vector databases (e.g., Azure AI Search, Pinecone, etc.).
• Familiarity with Azure cloud services and enterprise-grade deployments.
• Hands-on experience with MCP and/or A2A agent communication frameworks.

Preferred Qualifications

• Direct experience with Azure AI Foundry and Copilot Studio.
• Experience integrating AI agents into enterprise workflows or SaaS platforms.
• Knowledge of prompt engineering, evaluation frameworks, and guardrails.
• Experience with CI/CD, MLOps, or AI observability.
• Understanding of security, identity, and compliance in enterprise AI systems.

Nice-to-Have

• Contributions to AI prototypes, internal platforms, or open-source projects.
• Experience moving AI solutions from prototype to production.
• Strong communication skills and ability to explain complex AI systems to non-experts.