About the Role

We are looking for a hands-on AI Engineer to help build and implement next-generation Agentic AI and Generative AI solutions for a major digital transformation initiative.

This is an engineering-focused role for someone who has gone beyond experimentation and has personally built, fine-tuned and deployed AI solutions into production. You will work extensively across Agentic AI, LLM fine-tuning, RAG, Python and production AI engineering.

What You’ll Be Doing

• Design and implement Agentic AI workflows, including tool/function calling, orchestration, automated decision flows and system integrations. • Build and deploy production-grade RAG pipelines, covering document ingestion, chunking, embeddings, vector search, retrieval and grounded generation. • Fine-tune LLMs using LoRA and/or QLoRA, including dataset preparation, training, evaluation and optimization. • Develop production AI applications and services using Python. • Integrate LLMs and AI agents with APIs, enterprise systems and data sources. • Implement evaluation frameworks and guardrails to measure model accuracy, quality, safety and reliability. • Containerize and deploy AI services using Docker, REST APIs, CI/CD and cloud platforms. • Monitor production AI systems for performance, latency, model quality and drift. • Collaborate with AI, data, engineering and product teams to turn complex requirements into working AI capabilities.

Must-Have Technical Experience

We are specifically looking for candidates who can demonstrate:

• Agentic AI: Hands-on implementation of AI agents or agentic workflows in real-world applications. • LLM Fine-Tuning: Must have personally fine-tuned LLMs using LoRA and/or QLoRA. This is a core requirement. • Production RAG: Must have personally built and deployed Retrieval-Augmented Generation solutions into production, including embeddings, retrieval and vector databases. • Python: Strong and currently hands-on Python development for AI/ML applications and production systems. • AI/ML Engineering: Approximately 3–5 years of hands-on AI/ML engineering experience, ideally within approximately 6 years of overall professional experience.

Technical Stack

Experience across a combination of:

Python | LangChain | LangGraph | LlamaIndex | Hugging Face Transformers | PEFT | LoRA | QLoRA | RAG | Vector Databases | PyTorch | TensorFlow | REST APIs | Docker | CI/CD | AWS | Azure | GCP