Ai ML Engineer

Role Overview

We are looking for an experienced AI Engineer with strong enterprise-level AI implementation experience. The ideal candidate should have hands-on experience designing, building, integrating, and deploying AI/ML and Generative AI solutions into real-world enterprise environments.

The candidate should be able to take AI solutions beyond PoC — from architecture and development through integration, deployment, scalability, monitoring, and production optimization.

Key Responsibilities

• Design, develop and deploy enterprise-grade AI/ML and Generative AI solutions.

• Build and integrate LLM-based applications, AI Agents and RAG solutions into enterprise workflows and applications.

• Work with models such as GPT, Llama, Claude, Mistral or equivalent foundation models.

• Design scalable RAG pipelines, embeddings and vector-search solutions.

• Develop AI Agents using LangChain, LangGraph, CrewAI, AutoGen or similar frameworks.

• Integrate AI capabilities with existing enterprise applications, APIs, databases and business systems.

• Optimize AI applications for performance, scalability, reliability, latency and cost.

• Implement model deployment, monitoring, evaluation and continuous improvement using MLOps practices.

• Build production-ready APIs, microservices and cloud-based AI applications.

• Work with cloud platforms such as AWS, Azure or GCP, along with Docker, Kubernetes and CI/CD.

• Collaborate with architects, engineering teams, product/business stakeholders and clients to translate business requirements into AI solutions.

• Where relevant, work on NLP, ASR/STT, TTS and voice-based AI applications.

Mandatory / Core Skills

• 5+ years in AI/ML Engineering / Applied AI / Generative AI
• Strong hands-on enterprise AI implementation / production deployment experience
• Python
• LLMs / Generative AI
• RAG, embeddings, vector databases
• AI Agents / Agentic AI
• Prompt engineering and LLM application development
• Model deployment and MLOps
• Cloud: AWS / Azure / GCP
• APIs, microservices and enterprise application integration
• Docker / Kubernetes / CI/CD

Good to Have

• PyTorch / TensorFlow / Hugging Face
• Fine-tuning / LoRA / PEFT
• NLP
• ASR/STT – Whisper, Deepgram, Azure Speech
• TTS – ElevenLabs, Coqui, Google TTS
• LangChain / LangGraph / CrewAI / AutoGen
• FastAPI / Flask
• Experience with enterprise-scale AI platforms or large organizations