AI / ML Engineer

We are seeking an experienced AI/ML Engineer with strong expertise in Generative AI (Mandatory) to build and deploy intelligent AI-powered products. The ideal candidate should have hands-on experience with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, model fine-tuning, and production-scale AI systems.

Responsibilities:

• Design, develop, and deploy AI/ML solutions with a strong focus on Generative AI.
• Build and optimise applications powered by Large Language Models (LLMs).
• Develop AI agents, copilots, chatbots, and workflow automation systems.
• Design and implement RAG pipelines using vector databases and embedding models.
• Fine-tune, evaluate, and optimise AI models for business use cases.
• Integrate AI models with enterprise applications and backend systems.
• Develop scalable model-serving and inference pipelines.
• Monitor model performance, accuracy, and reliability in production environments.
• Collaborate with product, engineering, and business teams to define AI-driven solutions.

Requirements:

• 4-6 years of experience in AI/ML engineering.
• Strong hands-on experience in Generative AI (Mandatory).
• Experience working with LLMs such as GPT, Claude, Gemini, Llama, Mistral, or similar models.
• Strong understanding of Prompt Engineering, RAG, Embeddings, and Vector Search.
• Experience building AI-powered applications in production environments.
• Proficiency in Python and modern AI/ML development frameworks.
• Experience with machine learning fundamentals, model training, evaluation, and deployment.
• Hands-on experience with cloud AI services on AWS, Azure, or GCP.

Technical Skills:

• Python, FastAPI, Flask, or similar frameworks.
• LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, or similar AI orchestration frameworks.
• Vector databases such as Pinecone, Weaviate, Milvus, Chroma, or FAISS.
• OpenAI, Anthropic, Gemini, AWS Bedrock, Azure OpenAI, or Vertex AI.
• Docker, Kubernetes, CI/CD, and MLOps practices.
• SQL and NoSQL databases.

Good to Have:

• Experience with model fine-tuning, LoRA, PEFT, or custom model training.
• Knowledge of multimodal AI systems (text, image, audio, video).
• Experience with AI evaluation frameworks, guardrails, and observability tools.
• Familiarity with MCP (Model Context Protocol) and multi-agent architectures.
• Experience working on enterprise-scale AI platforms.
• Strong problem-solving and analytical skills.
• Ability to take AI products from proof of concept to production.
• Experience building scalable, reliable, and secure AI systems.
• Strong communication and collaboration skills.