Senior Machine Learning Engineer

KS Smart Solutions; Chennai District; On-site; INR 3,000,000 - 5,500,000; Full time

Job summary

KS Smart Solutions in Chennai, India, is seeking a Senior Machine Learning Engineer to lead vision-based analytics pipelines. You will balance hands-on model development and edge deployment with mentoring a small ML team, delivering state-of-the-art computer vision models and real-time on-device inference.

You will design and train models for object detection, OCR, and segmentation, optimize for NVIDIA Jetson and CI/CD, and drive MLOps practices while ensuring production-grade Python code and

Qualifications

• 5+ years of industry experience in Machine Learning with CV in production.
• 1-2+ years leading or mentoring an engineering/data science team.
• Strong Python and scientific stack proficiency.

Responsibilities

• Define ML project scopes, timelines and architecture with product/engineering leadership.
• Design, train and evaluate Computer Vision models for detection, classification, segmentation, and OCR.
• Apply CV techniques (OpenCV, CNNs, YOLO, ViT) and state-of-the-art transformers.
• Deploy models to edge hardware (NVIDIA Jetson) with optimization (TensorRT, ONNX).
• Architect and maintain end-to-end MLOps pipelines; containerize with Docker; automate with CI/CD.

Skills

Python

NumPy/SciPy/Pandas

OpenCV

PyTorch/TensorFlow

CV/ML Architectures

Edge Deployment

MLOps

Team Leadership

CI/CD

Docker

Tools

NVIDIA Jetson

TensorRT

ONNX Runtime

Jetson SDKs

GitHub Actions

Job description

Role Name: Senior Machine Learning Engineer

Experience: 5-8 years

Role:

As a Senior ML Engineer and Team Lead, you will be the driving technical and operational force behind our vision-based analytics pipelines. You will operate as a "player-coach," balancing hands-on model development and edge deployment with the management of a small team of ML engineers. You will design state-of-the-art computer vision modelsranging from classic image processing to modern transformer architectures—and ensure they run efficiently on edge devices (NVIDIA Jetson) using modern MLOps and CI/CD practices.

Responsibilities

• Technical Road Mapping: Work closely with product and engineering leadership to define ML project scopes, timelines, and technical architecture.
• Model Development: Design, train, and evaluate Computer Vision models for object detection, image classification, segmentation, and Optical Character Recognition (OCR).
• Algorithm Selection: Apply the right tool for the job, balancing classic CV algorithms, standard Deep Learning approaches (CNNs, YOLO, etc.), and state-of-the-art Vision Transformers (ViT, DETR).
• Edge Optimization & Deployment: Optimize models for real-time inference on edge devices (NVIDIA Jetson) using frameworks like TensorRT, ONNX, or DeepStream.
• MLOps & Infrastructure: Architect and maintain end-to-end MLOps pipelines. Containerize applications using Docker and automate workflows using CI/CD pipelines.
• Production Monitoring: Implement telemetry and monitoring solutions to track model drift, hardware health, and inference performance on distributed edge devices in the field.
• Code Quality: Write clean, production-ready, and highly optimized Python code, and enforce these standards across your team through code reviews.
• Team Leadership & Management: Manage and mentor a small team of ML engineers. Guide their technical development, oversee task delegation, conduct 1-on-1s, and foster a culture of engineering excellence and collaboration.

Required Qualifications

• Experience: 5+ years of industry experience in Machine Learning, with a heavy focus on Computer Vision in production environments.
• Leadership: 1-2+ years of experience leading, managing, or formally mentoring an engineering or data science team.
• Programming: Strong proficiency in Python and its scientific computing ecosystem (NumPy, SciPy, Pandas).
• Computer Vision Expertise:
  • Solid grasp of classic CV techniques (OpenCV).
  • Deep understanding of DL architectures for detection and classification (PyTorch or TensorFlow).
  • Hands-on experience with OCR systems (e.g., Tesseract, EasyOCR, PaddleOCR).
  • Working knowledge of Transformer-based vision models.
• Edge Deployment: Proven track record of deploying models to edge hardware, specifically NVIDIA Jetson architecture. Experience with model quantization, pruning, and optimization (TensorRT, ONNX Runtime).
• MLOps & DevOps: Hands-on experience with Docker and building automated CI/CD pipelines (GitHub Actions, GitLab CI, or similar).
• Monitoring: Experience setting up monitoring, logging, and remote management for edge deployments.