Job Description

HCLTech is hiring Vision AI Engineer for Noida/Chennai location.

About HCLTech:

HCLTech is a global technology company, home to more than 218,000 people across 59 countries, delivering industry-leading capabilities centered around digital, engineering, cloud and AI, powered by a broad portfolio of technology services and products. We work with clients across all major verticals, providing industry solutions for Financial Services, Manufacturing, Life Sciences and Healthcare, Technology and Services, Telecom and Media, Retail and CPG, and Public Services. Consolidated revenues as of 12 months ending September 2024 totaled $13.7 billion. To learn how we can supercharge progress for you, visit hcltech.com.

The dedicated business unit – “AIoT & Industrial AI” leads the Industrial AI and (A)IoT Business end-to-end for HCL Technologies including overall product direction, new business models, ecosystem and pioneer customer engagements in partnership with Engineering and Application Services. We have a unique opportunity in the area of Industrial AI & IoT platform and services where HCL Technologies can lead business model transformations for customers.

Job Description:

Overall Experience: 6 to 13 yrs

Location: Noida/Chennai

Notice Period: Immediate/30 days

Role Overview

We are seeking a Senior Computer Vision Developer to design, build, and deploy vision-based AI solutions for real-world applications. The role requires deep hands-on experience with image/video analytics, deep learning model development, optimization, and deployment. You will work closely with AI Architects and data engineers to deliver high-performance, production-grade vision systems.

Experience Required

5–8 years of experience in AI/ML engineering, with 3+ years specialized in Computer Vision.
Hands-on deployment of vision models in production environments (edge or cloud).
Proven experience in optimizing models for real-time inference.
Strong track record of building vision AI solutions for real-world use cases (retail, healthcare, manufacturing, autonomous systems, surveillance, etc.).

Key Responsibilities

Model Development & Training
Implement and fine-tune state-of-the-art computer vision models for object detection, classification, segmentation, OCR, pose estimation, and video analytics.
Apply transfer learning, self-supervised learning, and multimodal fusion to accelerate development.
Experiment with generative vision models (GANs, Diffusion Models, ControlNet) for synthetic data augmentation and creative tasks.
Vision System Engineering
Develop and optimize vision pipelines from raw data preprocessing → training → inference → deployment.
Build real-time vision systems for video streams, edge devices, and cloud platforms.
Implement OCR and document AI for text extraction, document classification, and layout understanding.
Integrate vision models into enterprise applications via REST/gRPC APIs or microservices.
Optimization & Deployment
Optimize models for low-latency, high-throughput inference using ONNX, TensorRT, OpenVINO, CoreML.
Deploy models on cloud (AWS/GCP/Azure) and edge platforms (NVIDIA Jetson, Coral, iOS/Android).
Benchmark models for accuracy vs performance trade-offs across hardware accelerators.
Data & Experimentation
Work with large-scale datasets (structured/unstructured, multimodal).
Implement data augmentation, annotation pipelines, and synthetic data generation.
Conduct rigorous experimentation and maintain reproducible ML workflows.

Required Skills & Qualifications

Programming: Expert in Python; strong experience with C++ for performance-critical components.
Deep Learning Frameworks: PyTorch, TensorFlow, Keras.
Computer Vision Expertise:
Detection & Segmentation: YOLO (v5–v8), Faster/Mask R-CNN, RetinaNet, Detectron2, MMDetection, Segment Anything.
Vision Transformers: ViT, Swin, DeiT, ConvNeXt, BEiT.
OCR & Document AI: Tesseract, PaddleOCR, TrOCR, LayoutLM/Donut.
Video Understanding: SlowFast, TimeSformer, action recognition models.
3D Vision: PointNet, PointNet++, NeRF, depth estimation.
Generative AI for Vision: Stable Diffusion, StyleGAN, DreamBooth, ControlNet.
MLOps Tools: MLflow, Weights & Biases, DVC, Kubeflow.
Optimization Tools: ONNX Runtime, TensorRT, OpenVINO, CoreML, quantization/pruning frameworks.
Deployment: Docker, Kubernetes, Flask/FastAPI, Triton Inference Server.
Data Tools: OpenCV, Albumentations, Label Studio, FiftyOne.