Lead- Data Scientist + AI Engineer
RoundCircle · Bengaluru · onsite · Posted 3d ago
About a minute. You'll make an account on the way through.
About this role
About the Role
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)
Interview Mode: In-person interviews (Whitefield, Bangalore)
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
- 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
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