VS01723 - AI Engineer + Data Scientist

E4 Software Services Pvt Ltd.; Bengaluru; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

E4 Software Services Pvt Ltd. is seeking a senior ML/AI Architect to design and deploy end-to-end DL/RL systems from design to production. You will set governance, optimize inference pipelines, and scale training across distributed infra in the cloud.

You will lead technical solutions, mentor engineers, and collaborate with stakeholders to deliver measurable business impact through 6–10 production deployments.

Qualifications

• 7+ years of ML/AI with production DL/RL systems.
• Mastery of DL frameworks (PyTorch/TensorFlow/JAX) and strong applied math.
• 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.

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

ML/AI experience

Production DL/RL

PyTorch

TensorFlow

JAX

CNNs/Transformers

RL algorithms

Inference optimization

Docker/Kubernetes

Azure ML

MLOps

Lead & mentor

Tools

Docker

Kubernetes

Azure ML

AKS/ARO

TensorRT/ONNX/Triton

Job description

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