What are the responsibilities and job description for the Machine Learning Engineer position at Coforge?
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.