AI Engineer + Data Scientist - Lead _Bangalore

APPTAD, Inc.; Bengaluru; Hybrid; INR 4,000,000 - 7,000,000; Full time

Job summary

APPTAD, Inc. is seeking an AI Technical Lead with 8+ years of ML/AI experience to own enterprise AI solutions from design to production. You will set technical standards, drive governance, and guide a team of engineers across DL/RL deployments, model serving, and scalable infrastructure.

The role emphasizes leadership, stakeholder communication, and hands-on mentoring, with experience in PyTorch/TensorFlow, distributed training, and cloud-scale AI on Azure ML. Hybrid work in Bangalore.

Qualifications

• 8+ years in ML/AI with production DL/RL
• Strong mastery of DL frameworks (PyTorch/TF/JAX) and applied math
• DL across CNNs, transformers, sequence models; RL agents (PPO, SAC, TD3, CQL)
• Inference optimization 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 leadership, mentoring, and stakeholder communication
• Track record of 6–10 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); balance accuracy vs. latency
• Scale training/inference on Azure ML, AKS/ARO, and distributed infrastructure
• Lead technical solutioning, manage stakeholders, and mentor engineers

Skills

Production DL/RL

PyTorch

TensorFlow

Distributed training

MLOps CI/CD

Leadership & mentoring

Stakeholder communication

Docker Kubernetes

Azure ML

ONNX TensorRT Triton

Kubernetes

TL/LLM deployment

Education

Master's degree in CS/Data Science/Math

Tools

Docker

Kubernetes

Azure ML

PyTorch

TensorFlow

ONNX

TensorRT

Triton

AKS/ARO

Ray RLlib

Job description

• Skills- AI Engineer + Data Scientist - Lead
• Work Mode (Onsite / Hybrid / Remote) Hybrid
• Work Location - Bangalore
• Years of experience required for this role 8+ Years

Mandates :F2F for final round of interview.

AI Technical Lead

Experience: 8+ years in ML/AI (incl. DL/RL in production)
Reports To: Manager, AIML
Education: Master's (preferred) or Bachelor's in CS, Data Science, or Mathematics

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

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); 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 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.