Lead- Data Scientist + AI Engineer

RoundCircle; Bengaluru; Vor Ort; INR 5.000.000 - 7.000.000; Vollzeit

Zusammenfassung

RoundCircle in Bangalore (Whitefield) is seeking a player-coach leader to own enterprise-scale AI solutions from architecture through production. You will set standards, govern models, and drive optimization for latency-aware inference at scale.

The role requires 7+ years in ML/AI with production DL/RL, strong DL framework mastery, and hands-on leading teams to 6–10 deployments with measurable business impact. Immediate joiners preferred.

Qualifikationen

• 7+ years of ML/AI with production DL and/or RL systems.
• Mastery of DL frameworks (PyTorch/TensorFlow/JAX) with strong applied math.
• Experience with CNNs, transformers, 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 and 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 6-10 production deployments with measurable business impact.

Aufgaben

• 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, balancing accuracy and latency.
• Scale training and inference on Azure ML, AKS/ARO and distributed infrastructure.
• Lead technical solutioning, manage stakeholders and mentor engineers.

Kenntnisse

Production DL/RL

DL frameworks

Distributed training

Azure ML

Docker/Kubernetes

Model optimization

Leadership

Stakeholder mgmt

Ausbildung

Master's preferred

Bachelor's in CS/Data Science/Math

Tools

PyTorch

TensorFlow

JAX

TensorRT

ONNX

AKS/ARO

Docker

Kubernetes

Jobbeschreibung

Own the full lifecycle of enterprise-scale AI solutions, from architecture through production and set technical best practices, governance and standards across the team. A player-coach leadership role.

Experience: 7+ years in ML/AI (including DL/RL in production)

Location: Bangalore (Whitefield) - Work from office, 5 days a week

Notice Period: Immediate joiners preferred (0-15 days)

Education: Master's preferred, or Bachelor's in CS, Data Science or Mathematics

Please note: An online assessment is mandatory as part of the selection process.

Key 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), balancing accuracy and latency
• Scale training and inference on Azure ML, AKS/ARO and distributed infrastructure
• Lead technical solutioning, manage stakeholders and mentor engineers

Must-Have Skills

• 7+ years of 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 and 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 6-10 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
• Semiconductor, manufacturing or supply-chain domain experience