AI Senior Engineer

TELUS Digital; Bengaluru; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

Require 4+ years in robotics ML, strong Python, and experience with ROS/ROS2, Docker, and PostgreSQL.

Qualifications

• 4+ years in robotics ML, computer vision, or technical solutions engineering.
• Strong CV and perception — detection, segmentation, pose estimation, optical flow.
• Solid 3D geometry — SE(3) transformations, camera models, projection.
• Sensor calibration — intrinsic/extrinsic, hand‑eye, and multi‑modal alignment.
• LiDAR processing and sensor fusion, including LiDAR–camera calibration.

Responsibilities

• Own client engagements end to end — technical discovery, solution architecture, hands‑on build, and delivery.
• Translate ambiguous problems into perception and robotics ML architectures; lead demos and POC scoping.
• Design and build CV and perception systems — detection, segmentation, pose estimation, 3D geometry under SE(3).
• Own sensor calibration and fusion — including LiDAR–camera calibration and hand‑eye alignment.
• Design data collection pipelines using wrist/head‑mounted cameras, depth sensors, and IMU.
• Bridge simulation and deployment via sim setup, domain randomisation, and sim‑to‑real gap analysis.
• Evaluate, select, and integrate VLA models, and fine‑tune large pretrained models.
• Build supporting services — REST/gRPC APIs, PostgreSQL — to expose model inference and robotics services.
• Mentor engineers, drive principled decisions, translate research into client impact.

Skills

Robotics ML

Python (PyTorch)

CV & perception

Client-facing

Strong communication

Education

Robotics ML degree or equivalent

Tools

ROS/ROS2

Docker

PostgreSQL

CI/CD

Unity

Job description

Key Responsibilities

• Own client engagements end to end — technical discovery, solution architecture, hands‑on build, and delivery — acting as the technical authority on feasibility.
• Translate ambiguous real‑world problems into concrete perception and robotics ML architectures; lead demos and proof‑of‑concept scoping.
• Design and build CV and perception systems — detection, segmentation, pose estimation — on the 3D geometry underneath (SE(3), camera models, projection, coordinate frames).
• Own sensor calibration and fusion — intrinsic/extrinsic, hand‑eye, and LiDAR–camera — plus LiDAR processing (registration, ground segmentation, object detection).
• Design egocentric data collection pipelines using wrist/head‑mounted cameras, depth sensors, and IMU for robot learning.
• Bridge simulation and deployment via domain randomisation, sim setup, and sim‑to‑real gap analysis.
• Evaluate, select, and integrate VLA models, and fine‑tune large pretrained models under data and compute constraints.
• Build supporting services — agentic loops, REST/gRPC APIs, and PostgreSQL — to expose model inference and robotics services.
• Mentor engineers, drive principled decisions (diagnose before solving), and translate current research into practical client implications.

Required Qualifications

• 4+ years in robotics ML, computer vision, or technical solutions engineering.
• Strong CV and perception — detection, segmentation, pose estimation, optical flow.
• Solid 3D geometry — SE(3) transformations, camera models, projection, coordinate frames.
• Sensor calibration — intrinsic/extrinsic, hand‑eye, and multi‑modal alignment.
• LiDAR processing and sensor fusion, including LiDAR–camera calibration.
• Sim‑to‑real experience — domain randomisation and sim environments (Isaac Sim, MuJoCo, Gazebo, or Unity Robotics Hub).
• Conceptual grasp of VLA architectures and the current SOTA landscape.
• Strong Python (PyTorch); able to read and implement research papers.
• Prior client‑facing or presales technical experience.
• Clear communicator across technical and non‑technical audiences.

Preferred Qualifications

• Agentic systems and prompt engineering — tool‑use agents and multi‑step planners.
• API development (REST/gRPC, Docker, basic CI/CD) and PostgreSQL.
• ROS / ROS2 or equivalent robotics middleware.
• C# for Unity‑based simulation and tooling.
• Fine‑tuning large pretrained models under data/compute constraints.
• Edge/onboard optimisation — TensorRT, ONNX, quantisation.
• RL / RLHF, motion planning, kinematics, or URDF.
• Hands‑on with robot hardware (Franka, UR, Unitree, or similar).
• Research orientation — tracks arXiv, forms opinions from first principles.
• Publications, open‑source work, or notable projects in robot learning or Physical AI.

Technical Stack

• Languages: Python (PyTorch)
• Perception / 3D: OpenCV, Open3D, PCL, point cloud processing
• Simulation: Isaac Sim, MuJoCo, Gazebo, Unity Robotics Hub
• Robotics Middleware: ROS / ROS2
• Models: VLA models (RT‑2, OpenVLA, π₀, Octo), ViTs, multimodal, hosted and open‑source
• Deployment: REST/gRPC, Docker, PostgreSQL; TensorRT / ONNX for edge
• Core Areas: CV and perception, 3D geometry, calibration, LiDAR, sensor fusion, sim‑to‑real, agentic systems

Success Measures

• Client engagements delivered with clear architecture, working perception/robotics systems, and evaluation coverage.
• Reusable solution patterns and accelerators created from recurring engagement needs.
• Robust calibration, data collection, and sim‑to‑real workflows that transfer reliably to real hardware.
• Sound VLA and model‑selection/fine‑tuning decisions, grounded in the current SOTA landscape.
• Effective mentorship and principled technical guidance across the Technical Solutions team.

Equal Opportunity Employer Statement

At TELUS Digital, we are proud to be an equal opportunity employer and are committed to creating a diverse and inclusive workplace. All aspects of employment, including the decision to hire and promote, are based on applicant's qualifications, merits, competence and performance without regard to any characteristic related to diversity.