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Machine Learning (ML) / MLOps Engineer Job Description

An ML/MLOps Engineer is responsible for designing, developing, and deploying machine learning models and pipelines. Here's a brief overview:

Key Responsibilities

- Model Development: Design and develop machine learning models using various algorithms and techniques.

- MLOps: Implement and manage machine learning pipelines, including data preparation, model training, and deployment.

- Model Deployment: Deploy machine learning models to production environments, ensuring scalability and reliability.

- Model Monitoring: Monitor model performance and retrain models as needed to maintain accuracy and relevance.

- Collaboration: Collaborate with cross-functional teams, including data scientists, product managers, and engineers.

Technical Skills

- Machine Learning: Strong understanding of machine learning concepts, including supervised and unsupervised learning, deep learning, and reinforcement learning.

- Programming: Proficiency in programming languages like Python, R, or Julia.

- ML Frameworks: Experience with machine learning frameworks like TensorFlow, PyTorch, or scikit-learn.

- MLOps Tools: Familiarity with MLOps tools like TensorFlow Extended (TFX), MLflow, or Kubeflow.

- Cloud Platforms: Experience with cloud platforms like AWS, Azure, or GCP.

• Design and implement cloud solutions, build MLOps on Azure
• Build CI/CD pipelines orchestration by GitLab CI, GitHub Actions, Circle CI, Airflow or similar tools
• Data science model review, run the code refactoring and optimization, containerization, deployment, versioning, and monitoring of its quality
• Data science models testing, validation and tests automation
• Deployment of code and pipelines across environments
• Model performance metrics
• Service performance metrics
• Communicate with a team of data scientists, data engineers and architect, document the processes

3+ years’ experience as Python Developer / Designer and Machine learning 2. Performance Improvement understanding and able to write effective, scalable code 3. security and data protection solutions 4. Expertise in at least one popular Python framework (like Django, Flask or Pyramid) 5. Knowledge of object-relational mapping (ORM) 6. Familiarity with front-end technologies (like JavaScript and HTML5

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.)