Senior Software Engineer (AI)

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.