Lead Data Scientist

UST; Bengaluru; Hybrid; INR 3,500,000 - 6,500,000; Full time

Job summary

UST is seeking a senior ML/AI leader to architect end-to-end DL/RL and agentic AI solutions from design to production. You will set standards, governance, and evaluation frameworks across the team.

You will optimize models for production inference (TensorRT/ONNX/Triton), scale training and inference on Azure ML, AKS/ARO, and distributed infra, and mentor engineers while managing stakeholders.

Qualifications

• 7+ years ML/AI with production DL/RL experience.
• Expert in DL frameworks PyTorch, TensorFlow or JAX.
• Experience with CNNs, transformers and RL agents (PPO, SAC, TD3, CQL).
• Inference optimization using TensorRT/ONNX/Triton.
• Model serving and containerized deployment at scale (Docker/Kubernetes, AKS/ARO).
• Cloud-scale training on Azure ML with distributed training and MLOps/CI-CD.
• Ability to define standards, governance, and evaluation frameworks across a team.
• Strong leadership, mentoring, and stakeholder communication.
• Track record of 610 production deployments with measurable business impact.

Responsibilities

• Architect end-to-end DL/RL and agentic AI solutions from design to production.
• Set technical standards, governance, and evaluation frameworks across the team.
• Optimize models for production inference (TensorRT/ONNX/Triton) with accuracy vs. latency balance.
• Scale training/inference on Azure ML, AKS/ARO, and distributed infrastructure.
• Lead technical solutioning, manage stakeholders, and mentor engineers.

Skills

7+ years ML/AI with production DL/RL

DL frameworks mastery (PyTorch, Tensor

Tools

TensorRT

ONNX

Triton

Docker/Kubernetes

Azure ML

AKS/ARO

TensorFlow

PyTorch

JAX

Job description

Role & responsibilities

• Architect end-to-end DL/RL and agentic AI solutions from design to production.


• Set technical standards, governance, and evaluation frameworks across the team.


• Optimize models for production inference (TensorRT/ONNX/Triton); balance accuracy vs. latency.


• Scale training/inference on Azure ML, AKS/ARO, and distributed infrastructure.


• Lead technical solutioning, manage stakeholders, and mentor engineers.

Must-Have Skills

• 7+ years ML/AI with production DL and/or RL systems.


• Mastery of DL frameworks (PyTorch/TensorFlow/JAX) and strong applied math (linear algebra, probability, optimization).


• Deep learning across CNNs, transformers, and sequence models; RL agents (PPO, SAC, TD3, CQL).


• Inference optimization (TensorRT/ONNX/Triton) and accuracy vs. latency benchmarking.


• Model serving and containerized deployment at scale (Docker/Kubernetes, AKS/ARO).


• Cloud-scale training/inference on Azure ML with distributed training and MLOps/CI-CD.


• Ability to define standards, governance, and evaluation frameworks across a team.


• Proven technical leadership, mentoring, and stakeholder communication.


• Track record of 610 production deployments with measurable business impact.

Nice to Have

• Agentic AI architecture (LangGraph, AutoGen, CrewAI) and RAG pipeline design.


• RL libraries (Ray RLlib, Stable-Baselines3, Gymnasium); multi-agent RL and simulation (MuJoCo).


• Model compression/quantization and GPU-efficiency optimization.


• Agentic platform evaluation (Azure AI Foundry, AWS Bedrock, Databricks AgentBricks).


• Optimization/OR background (LP/MIP, Gurobi/CPLEX); Julia/SciML exposure.


• Semiconductor, manufacturing, or supply-chain domain experience.