Role Overview

We are looking for a Senior AI/ML Engineer — Generative AI & Agentic Systems to design, develop, and productionize enterprise-grade Generative AI, RAG, and Agentic AI solutions.
The candidate will work closely with the AI Architect / Technical Lead to translate business requirements into scalable AI solutions and will be responsible for hands-on implementation, integration, evaluation, optimization, and deployment.
This role is ideal for an engineer who enjoys building real-world AI applications using LLMs, RAG pipelines, AI Agents, tool calling, vector databases, orchestration frameworks, and cloud technologies.

Key Responsibilities

  • Design and develop production-ready Generative AI and LLM-based applications.
  • Build scalable Retrieval-Augmented Generation (RAG) pipelines using embeddings, vector databases, reranking, retrieval, and contextual generation.
  • Develop Agentic AI workflows using frameworks such as LangGraph and LangChain.
  • Implement multi-agent workflows involving planning, reasoning, tool calling, delegation, memory, and orchestration.
  • Integrate LLMs from OpenAI, Anthropic, Google Gemini, and other providers.
  • Develop backend services and AI APIs using Python, FastAPI, and REST APIs.
  • Design and implement LLM evaluation frameworks to measure accuracy, relevance, groundedness, hallucination, latency, and other quality metrics.
  • Implement AI guardrails, validation, safety controls, structured outputs, and responsible AI practices.
  • Work with PostgreSQL, vector databases, and graph databases to build AI knowledge systems.
  • Containerize and deploy AI applications using Docker and cloud platforms such as Azure/AWS.
  • Optimize LLM applications for performance, scalability, reliability, and cost.
  • Collaborate with architects, frontend/backend engineers, DevOps, and business stakeholders.
  • Participate in technical design, code reviews, troubleshooting, and production support.
  • Stay current with emerging developments in LLMs, Agentic AI, RAG, open-source models, and AI infrastructure.

Must-Have Skills

Programming & Backend

  • Strong hands-on experience with Python
  • FastAPI
  • REST API development
  • Backend application architecture and development

Generative AI & LLM

  • LLM application development
  • RAG architecture and implementation
  • Prompt engineering
  • Embeddings and semantic search
  • Vector databases
  • LLM integration and orchestration
  • Experience with one or more of:
    • OpenAI
    • Anthropic
    • Google Gemini

Agentic AI

  • Hands-on experience building AI Agents
  • LangGraph and/or LangChain
  • Tool/function calling
  • Agent workflows and orchestration
  • Multi-agent systems
  • Agent state and memory management

Data & Infrastructure

  • PostgreSQL
  • Vector databases
  • Docker
  • Azure and/or AWS
  • API integration and microservices

AI Quality & Security

  • LLM evaluation
  • RAG evaluation
  • Hallucination detection/mitigation
  • AI guardrails
  • Structured output and schema validation
  • Understanding of AI security and responsible AI practices

Preferred / Good-to-Have Skills

  • Neo4j / GraphRAG
  • Model Context Protocol (MCP)
  • vLLM
  • Ollama
  • Open-source LLMs such as Llama, Qwen, Mistral, etc.
  • LoRA / parameter-efficient fine-tuning
  • Model fine-tuning
  • Kubernetes
  • CI/CD and Azure DevOps/GitHub Actions
  • React / Next.js
  • Redis or other caching technologies
  • Experience with cloud-based AI/ML infrastructure
  • Experience optimizing inference performance and LLM costs

What We Are Looking For

  • Strong problem-solving and analytical skills.
  • Ability to convert business requirements into practical AI solutions.
  • Strong understanding of LLM architecture and modern GenAI patterns.
  • Ability to write clean, maintainable, production-quality code.
  • Comfortable working in a fast-moving AI engineering environment.
  • Ability to independently investigate new AI technologies and implement POCs/prototypes.
  • Good understanding of software engineering principles, APIs, databases, security, and deployment.
  • Strong communication and collaboration skills.
  • Ability to work closely with an AI Architect / Technical Lead while taking ownership of implementation.

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