Role & responsibilities

A. Roles and Responsibilities

1. Coordinate model evaluation and benchmarking activities under approved technical parameters and timelines.
2. Track model performance metrics, including accuracy, precision, recall, F1 score, BLEU / ROUGE, latency, throughput, hallucination rate, robustness, bias, toxicity, safety, multilingual capability and inference cost.
3. Monitor model development progress across training, fine-tuning, evaluation, validation, deployment-readiness and post-deployment monitoring.
4. Track MLOps activities, including model versioning, experiment logs, evaluation records, CI/CD readiness, API readiness, monitoring dashboards and retraining requirements.
5. Review compute utilisation inputs relating to GPU / TPU / HPC / cloud usage, distributed training, storage, inference infrastructure and cost optimisation.
6. Coordinate with startups, technology partners, evaluation agencies and compute providers for timely submission of technical reports and benchmarking results.
7. Prepare technical MIS dashboards,



evaluation summaries, compute review notes and progress updates.
8. Identify performance gaps, compute bottlenecks, deployment risks and MLOps-related dependencies.

B. Education B.E. / B.Tech / M.Tech / MCA / MBA in Computer Science, IT, AI/ML, Data Science, Electronics & Communication, Computer Engineering, Software Engineering, Business Analytics or equivalent related discipline from a recognised University / Institution.

C. Desirable Skills & Experience

1. Total Experience: 6 years in AI/ML projects, model evaluation, MLOps, or software systems, cloud projects, data platforms or technical programme management.
2. Understanding of AI/ML lifecycle, model benchmarking, inference, deployment, monitoring and retraining.
3. Familiarity with Git, MLflow, Weights & Biases, Docker, Kubernetes, CI/CD, APIs, cloud platforms, GPUs / TPUs / HPC and monitoring dashboards.

📌 MLOps Engineer (Delhi)
🏢 National Institute for Smart Government (NISG)
📍 Delhi