Machine Learning Engineer (Agentic AI & LLMs)

Job Summary

We are seeking a hands-on Machine Learning Engineer with strong expertise in LLMs, Agentic AI frameworks, and MCP-based architectures. The ideal candidate will have practical experience designing and deploying agentic flows that integrate RAG pipelines, knowledge bases, and multi-database interactions. This role requires a self-starter who can not only deliver robust solutions but also actively contribute to presales discussions, customer enablement, and quick POCs to demonstrate value.

Job Roles & Responsibilities

• Agentic AI & LLM Development
• Design, implement, and optimize agentic workflows using LangChain, LangGraph, n8n, and related orchestration tools.
• Setup and manage MCP servers and integrate them into agent-driven pipelines.
• Develop RAG (Retrieval-Augmented Generation) solutions leveraging vector databases, relational databases, and MongoDB.
• Implement web crawling and external MCP services (e.g., Tavily) to enhance agent capabilities.
• Knowledge Base Engineering
• Build knowledge repositories from text, audio, and video sources using embeddings, transcription, and summarization pipelines.
• Enable multi-modal knowledge extraction for downstream agent decision-making and summarization.
• Proof of Concept (POC) & Presales
• Rapidly prototype solutions to showcase feasibility and demonstrate agentic AI architectures to clients.
• Collaborate with sales and solution engineering teams to support presales activities, including architecture walkthroughs, technical demos, and proposal inputs.
• Provide thought leadership on agentic AI best practices and tool integrations.
• Integration & Tooling
• Work with APIs, vector DBs (Pinecone, Weaviate, FAISS, etc.), relational databases, and NoSQL stores (MongoDB).
• Enable smooth data flow across enterprise systems to empower AI agents.
• Ensure secure, scalable, and efficient deployment of AI pipelines in enterprise contexts.

Cultural Expectations

• Strong hands-on expertise with LLMs (OpenAI, Anthropic, or open-source models) and Agentic AI frameworks.
• Proven experience in building agentic flows using LangChain, LangGraph, n8n.
• Solid knowledge of MCP server setup and integration with agent workflows.
• Experience in RAG architecture, vector databases, and multi-database interactions (SQL, MongoDB).
• Practical exposure to web crawling and MCP integrations (e.g., Tavily, custom MCP agents).
• Proficiency in building knowledge bases from structured/unstructured content (text, audio, video).
• Ability to deliver rapid POCs and guide customers on architecture & integration strategy.
• Familiarity with cloud platforms (Azure, AWS, GCP) for AI/ML deployment.
• Strong problem-solving skills and a self-starter mindset.

Hiring Process

• Interview with hiring manager to assess technical skills.
• Technical interview with senior member from the technical team.
• Final interview with Tech Lead/CTO.