AI Engineer

Role Overview

The AI Engineer designs, builds, and operationalises machine learning and generative AI systems, taking them from prototype to production. The role covers model development, LLM and agentic application engineering, and the data and deployment pipelines that keep these systems accurate, scalable, and reliable.

Responsibilities

• Build, evaluate, and deploy machine learning and deep learning models for production.
• Develop LLM-based and agentic applications, including RAG pipelines and tool use.
• Fine-tune and optimise models for accuracy, latency, and cost using PEFT and quantisation.
• Design evaluation frameworks, benchmarks, and guardrails to safeguard model quality.
• Build and automate data pipelines for training, inference, and model monitoring.
• Package and serve models as scalable APIs with containerisation, CI/CD, and MLOps.
• Stay updated with advancements in AI/ML and apply them to practical use cases.

Requirements

• B.E./B.Tech, M.Tech, B.Sc., M.Sc., MCA, or an equivalent degree in Computer Science, Information Technology, Engineering, Mathematics, Statistics, or a related quantitative field.
• Strong programming skills in Python.
• Experience with ML/DL frameworks (e.g., scikit-learn, PyTorch, or TensorFlow).
• Hands-on experience with LLM APIs, prompt engineering, and agent frameworks (e.g., LangChain, LangGraph, MCP).
• Working knowledge of RAG, embeddings, and vector databases (e.g., pgvector, FAISS, or Pinecone).
• Experience serving models as APIs (FastAPI, Docker) with MLOps practices (MLflow, model versioning, monitoring).
• Proficiency with SQL and data pipeline tooling for large datasets.
• Experience with at least one cloud platform (AWS, Azure, or GCP).

Skills

• Python
• PyTorch
• LangChain
• AWS
• Docker

Benefits

• Opportunity to work with a dynamic and fast-paced engineering IT organization.
• Be part of a company that is passionate about transforming product development with technology.