IT Developer - LLM Engineer

Overview

Design, evaluate, optimize and operationalize enterprise Applied AI and Large Language Model systems across the full model lifecycle, partnering with platform, data, security and domain teams.

What you'll do

• Design and implement training, fine-tuning and adaptation pipelines for open-weight Large Language Models using supervised fine-tuning, LoRA, QLoRA and Parameter-Efficient Fine-Tuning (PEFT) techniques.
• Build and curate high-quality datasets from operational data sources, incidents, runbooks, logs, knowledge articles and validated interactions for model training and evaluation.
• Develop model evaluation frameworks covering reasoning, instruction following, hallucination detection, tool calling, safety, robustness and structured output quality.
• Evaluate and optimize retrieval-augmented generation systems through retrieval analysis, embedding model comparison, reranking, chunking strategies and groundedness assessment.
• Design and execute agent and tool-calling benchmarks that measure task completion, tool selection accuracy, failure recovery and operational safety.
• Optimize model serving performance through quantization, batching strategies, cache management, inference tuning and serving-stack evaluation.
• Implement experiment tracking, model governance, lineage management, release gating and deployment workflows using enterprise MLOps practices.
• Establish production monitoring, quality drift detection, observability metrics, security testing and rollback criteria for deployed AI solutions.

What you'll need

• 3+ years of IT experience with a Bachelor's Degree in Engineering, MCA, or MSc.
• 3+ years of software engineering or machine learning experience, including 2+ years of hands-on Generative AI or Large Language Model development.
• Experience with Python, PyTorch, Hugging Face Transformers, Datasets, PEFT and model fine-tuning workflows.
• Experience implementing Large Language Model evaluation, retrieval-augmented generation, model benchmarking and experiment tracking frameworks.
• Experience with Linux, Docker, Kubernetes, Git, REST APIs and cloud or GPU-based model deployment environments.
• Experience with MLflow or Weights & Biases for experiment tracking, model lifecycle management and artifact versioning.

Nice to have

• Experience with direct preference optimization (DPO), TRL, DeepSpeed, Fully Sharded Data Parallel (FSDP) or distributed training techniques.
• Experience deploying and optimizing models using vLLM, NVIDIA TensorRT-LLM, SGLang or comparable inference-serving platforms.
• Experience with RAGAS, DeepEval, TruLens or similar evaluation and observability frameworks.
• Experience in AI Ops, IT service management, incident management, observability platforms, knowledge systems or automated remediation workflows.
• Experience implementing OpenTelemetry, Prometheus, Grafana, model monitoring, security testing and production release governance.

Details

• Location: Nanakramguda, Hyderabad, India.
• Eligible for the Medtronic Incentive Plan (MIP), a short-term incentive.