AI Engineer

Role Overview

We are looking for an experienced Senior AI Engineer / Lead AI Engineer with 10+ years of overall technology experience and strong hands-on expertise in AI/ML, Generative AI, LLMs, RAG, Agentic AI, Python, and Cloud.

The candidate will be responsible for architecting, developing, deploying, and scaling enterprise-grade AI solutions and should be capable of taking AI initiatives from concept and architecture through production deployment and optimization.

The role requires a strong combination of hands-on engineering, solution architecture, technical leadership, and stakeholder management.

Key Responsibilities

• Architect and develop scalable AI/ML and Generative AI solutions for enterprise use cases.
• Design and implement LLM-powered applications, RAG systems, AI agents, and multi-agent architectures.
• Build production-grade Agentic AI workflows involving reasoning, planning, tool/function calling, memory, orchestration, and enterprise API integration.
• Design end-to-end RAG architectures, including document ingestion, chunking, embeddings, vector search, hybrid search, retrieval optimization, re-ranking, and response generation.
• Work with LLMs such as OpenAI, Azure OpenAI, Anthropic, Gemini, Llama, Mistral, or equivalent models.
• Implement advanced Prompt Engineering and Context Engineering techniques.
• Develop AI applications using LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, or equivalent frameworks.
• Design and integrate Vector Databases / Vector Search solutions.
• Develop scalable AI APIs and microservices using Python, FastAPI, Flask, or equivalent technologies.
• Integrate AI solutions with enterprise applications, databases, APIs, and data platforms.
• Establish LLM evaluation and testing frameworks for accuracy, grounding, hallucination, relevance, safety, latency, and cost.
• Implement AI security, governance, guardrails, access control, data privacy, and responsible AI practices.
• Deploy and operate AI solutions across Azure/AWS/GCP cloud environments.
• Drive production readiness, including CI/CD, Docker, monitoring, logging, observability, scalability, and performance optimization.
• Optimize AI systems for latency, throughput, reliability, model performance, and infrastructure cost.
• Evaluate emerging AI technologies and identify opportunities for enterprise adoption.
• Lead technical discussions with architects, engineering teams, product teams, and business stakeholders.
• Mentor senior and junior engineers and establish engineering best practices.
• Conduct technical reviews and ensure adherence to architecture, coding, security, and development standards.
• Build reusable AI frameworks, accelerators, components, and reference architectures.

Mandatory Skills

• 10+ years of overall experience in Software Engineering / AI / ML / Data Science / Technology.
• Strong hands-on Python development experience.
• Strong hands-on experience in AI/ML engineering.
• Strong experience with Generative AI and Large Language Models (LLMs).
• Proven experience designing and implementing RAG architectures.
• Strong experience with Agentic AI / AI Agents / Multi-Agent Systems.
• Strong Prompt Engineering / Context Engineering experience.
• Experience with LLM orchestration frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, or CrewAI.
• Experience with OpenAI / Azure OpenAI / Anthropic / Gemini / open-source LLMs.
• Strong experience with Vector Databases and semantic search.
• Experience building REST APIs / microservices using FastAPI or Flask.
• Strong knowledge of Machine Learning / Deep Learning concepts.
• Experience deploying AI solutions on AWS / Azure / GCP.
• Strong understanding of AI system architecture, distributed systems, scalability, and system design.
• Experience with Docker, Git, CI/CD, testing, monitoring, and production support.
• Strong technical leadership and stakeholder-management skills.

Good to Have

• MCP / Model Context Protocol experience.
• Experience with LLM evaluation and observability tools such as LangSmith, RAGAS, Promptfoo, or equivalent.
• Experience with fine-tuning, SFT, LoRA, QLoRA, PEFT, RLHF/DPO.
• Knowledge of GraphRAG, Knowledge Graphs, Neo4j, or semantic search.
• Experience with MLflow, Kubeflow, SageMaker, Azure ML, or equivalent MLOps pl…