2 Graphics Processing Unit (GPU) Jobs in Delhi, NCR and Gurgaon | Graphics Processing Unit (GPU) Job openings in Delhi, NCR and Gurgaon

Designation: Lead MLOps Engineer (GPU Optimization)

About CloudKeeper: 

CloudKeeper is a cloud cost optimization partner that combines the power of group buying & commitments management, expert cloud consulting & support, and an enhanced visibility & analytics platform to reduce cloud cost & help businesses maximize the value from AWS, Microsoft Azure, & Google Cloud. 

A certified AWS Premier Partner, Azure Technology Consulting Partner, Google Cloud Partner, and FinOps Foundation Premier Member, CloudKeeper has helped 350+ global companies save an average of 20% on their cloud bills, modernize their cloud set-up and maximize value — all while maintaining flexibility and avoiding any long-term commitments or cost. 

CloudKeeper hived off from TO THE NEW, a digital technology service company with 2500+ employees and an 8-time GPTW winner. 

To know more, please visit - https://www.cloudkeeper.com/ 

Responsibilities:

  - Drive R&D and engineering for AI Infrastructure optimization within CloudKeeper's FinOps for AI platform — building the Tuner AI / Commit AI capability on GPU and ML workloads

  - Design and build optimization engines for GPU right-sizing, idle shutdown, spot migration with checkpoint/resume automation, inference batching, quantization, and model placement

  - Extend the optimization stack to LLM-era workloads — caching, model routing, dynamic batching, prompt optimization, RAG-aware architectures

  - Partner with the Lens AI team to translate GPU and ML workload signals into actionable, dollar-quantified optimization recommendations for customers

  - Work cross-functionally with product, platform, and customer success teams to ship optimization features end-to-end (data ingestion → optimization engine → customer-facing recommendation)

  - Lead technical direction for AI workload optimization, set engineering standards, and mentor the ML / MLOps engineering bench as the AI Infrastructure pillar scales

  - (Lead level) Hire, ramp, and grow a team of ML infrastructure engineers as headcount expands

  Must Have:

  - B.E / B.Tech / M.Tech / MCA with 7+ years of hands-on engineering experience

  - Production experience with GPU workloads — has measurably optimized GPU utilization,  throughput, or cost in a real production environment, not just academic / lab work

  - Strong performance engineering background — must come ready with a concrete optimization story including before/after metrics (latency, throughput, or cost reduction)

  - Strong Python + Linux + systems fundamentals

  - Solid understanding of the ML model lifecycle — training, serving, inference — able to reason about what is running on the GPU and why

  - MLOps fluency — model deployment, monitoring, observability, GPU cluster operations

  - Hands-on with cloud GPU instances (AWS P5 / G6, Azure ND series, GCP A3, or equivalent) and Kubernetes-based GPU orchestration (EKS / AKS / GKE GPU node pools, Karpenter, Run:ai, NVIDIA GPU Operator, or similar)

  - Familiarity with at least one modern LLM inference framework — vLLM, TGI, Triton, SGLang, Ray Serve, or BentoML

  - Strong communication skills — able to translate deep technical optimization into customer / business outcomes

  - (Lead level) Experience managing or technically leading a team of 3+ engineers

  Good to Have:

  - Deep LLM-era optimization expertise — KV caching, semantic caching, model routing, dynamic batching, quantization (FP16 → INT8 → INT4), model distillation, structured outputs

  - Familiarity with LLM workload patterns — RAG, agents, embeddings, vector databases (Pinecone, Weaviate, Qdrant)

  - CUDA, NCCL, mixed-precision training and inference

  - Experience with managed ML training platforms — SageMaker, Azure ML, Vertex AI, Databricks Mosaic

  - Exposure to GPU-native clouds — CoreWeave, Lambda Labs, RunPod, Crusoe

  - Open source contributions to ML infrastructure projects — vLLM, llama.cpp, TGI, Ray, Triton, KubeRay

  - Adjacent experience in cloud cost optimization / FinOps — Spot.io, ScaleOps, Granulate, CAST AI

  - Comfort with Agile methodology and modern engineering practices (CI/CD, code review, observability)

JioTesseract, a digital arm of Reliance Industries, is India's leading and largest AR/VR organization with the mission to democratize mixed reality for India and the world. We make products at the cross of hardware, software, content and services with focus on making India the leader in spatial computing. We specialize in creating solutions in AR, VR and AI, with some of our notable products such as JioGlass, JioDive, 360 Streaming, Metaverse, AR/VR headsets for consumers and enterprise space.

Mon-fri role, In office, with excellent perks and benefits!

Position Overview

We are seeking a Software Architect to lead the design and development of high-performance robotics and AI software stacks utilizing NVIDIA technologies. This role will focus on defining scalable, modular, and efficient architectures for robot perception, planning, simulation, and embedded AI applications. You will collaborate with cross-functional teams to build next-generation autonomous systems 9

Key Responsibilities:

1. System Architecture & Design

● Define scalable software architectures for robotics perception, navigation, and AI-driven decision-making.

● Design modular and reusable frameworks that leverage NVIDIA’s Jetson, Isaac ROS, Omniverse, and CUDA ecosystems.

● Establish best practices for real-time computing, GPU acceleration, and edge AI inference.

2. Perception & AI Integration

● Architect sensor fusion pipelines using LIDAR, cameras, IMUs, and radar with DeepStream, TensorRT, and ROS2.

● Optimize computer vision, SLAM, and deep learning models for edge deployment on Jetson Orin and Xavier.

● Ensure efficient GPU-accelerated AI inference for real-time robotics applications.

3. Embedded & Real-Time Systems

● Design high-performance embedded software stacks for real-time robotic control and autonomy.

● Utilize NVIDIA CUDA, cuDNN, and TensorRT to accelerate AI model execution on Jetson platforms.

● Develop robust middleware frameworks to support real-time robotics applications in ROS2 and Isaac SDK.

4. Robotics Simulation & Digital Twins

● Define architectures for robotic simulation environments using NVIDIA Isaac Sim & Omniverse.

● Leverage synthetic data generation (Omniverse Replicator) for training AI models.

● Optimize sim-to-real transfer learning for AI-driven robotic behaviors.

5. Navigation & Motion Planning

● Architect GPU-accelerated motion planning and SLAM pipelines for autonomous robots.

● Optimize path planning, localization, and multi-agent coordination using Isaac ROS Navigation.

● Implement reinforcement learning-based policies using Isaac Gym.

6. Performance Optimization & Scalability

● Ensure low-latency AI inference and real-time execution of robotics applications.

● Optimize CUDA kernels and parallel processing pipelines for NVIDIA hardware.

● Develop benchmarking and profiling tools to measure software performance on edge AI devices.

Required Qualifications:

● Master’s or Ph.D. in Computer Science, Robotics, AI, or Embedded Systems.

● Extensive experience (7+ years) in software development, with at least 3-5 years focused on architecture and system design, especially for robotics or embedded systems.

● Expertise in CUDA, TensorRT, DeepStream, PyTorch, TensorFlow, and ROS2.

● Experience in NVIDIA Jetson platforms, Isaac SDK, and GPU-accelerated AI.

● Proficiency in programming languages such as C++, Python, or similar, with deep understanding of low-level and high-level design principles.

● Strong background in robotic perception, planning, and real-time control.

● Experience with cloud-edge AI deployment and scalable architectures.

Preferred Qualifications

● Hands-on experience with NVIDIA DRIVE, NVIDIA Omniverse, and Isaac Gym

● Knowledge of robot kinematics, control systems, and reinforcement learning

● Expertise in distributed computing, containerization (Docker), and cloud robotics

● Familiarity with automotive, industrial automation, or warehouse robotics

● Experience designing architectures for autonomous systems or multi-robot systems.

● Familiarity with cloud-based solutions, edge computing, or distributed computing for robotics

● Experience with microservices or service-oriented architecture (SOA)

● Knowledge of machine learning and AI integration within robotic systems

● Knowledge of testing on edge devices with HIL and simulations (Isaac Sim, Gazebo, V-REP etc.)