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.