Campus-Trainee

Candidates typically face a three‑stage process: an online aptitude test lasting 30‑40 minutes, followed by a technical interview that probes coding ability, data structures, and core computer‑vision concepts, and finally an HR round assessing cultural fit and communication. The difficulty is moderate; thorough preparation of coding fundamentals and vision basics greatly improves success.

HCLTech promotes a collaborative and flexible work culture where employees are encouraged to innovate and upskill continuously. The company offers clear growth trajectories, mentorship programs and a balanced approach to work‑life integration, especially for early‑career professionals.

B.Tech/B.E. or M.Tech in Computer Science, Information Technology, Electronics & Communication, or related streams; Minimum CGPA/percentage 6.5/70%; Graduation batch 2023‑2025; No active backlogs at the time of joining; Must be a fresh graduate (0‑1 year experience).

Round 1: Online Aptitude Test → Round 2: Technical Interview (coding & vision concepts) → Round 3: HR Interview

HCLTech is a global technology powerhouse with more than 223,000 employees spread across 60 countries. The company delivers end‑to‑end digital, engineering, cloud and AI services to a wide spectrum of industries, ranging from financial services and manufacturing to healthcare and public sector. With a revenue of $14.8 billion for the fiscal year ending June 2026, HCLTech has consistently been recognised for its innovation‑driven culture and commitment to employee growth. The firm’s "Employees First" philosophy ensures that talent is empowered with continuous learning, flexible work models and clear career pathways. The Campus‑Trainee role is positioned as an entry‑level Computer Vision Engineer II focused on Vision Model Development & Automation. Fresh graduates will be immersed in building production‑ready computer‑vision pipelines that address real‑world challenges in surveillance, industrial automation and smart spaces. The role blends deep algorithmic knowledge with software‑engineering rigor, enabling candidates to see their models move from research notebooks to scalable edge‑cloud deployments. Key Responsibilities: 1. Design and implement end‑to‑end vision pipelines covering ingestion, pre‑processing, inference, post‑processing and metadata generation. 2. Develop and fine‑tune state‑of‑the‑art algorithms for object detection, tracking, re‑identification, segmentation, anomaly detection and OCR. 3. Build automated MLOps workflows for training, evaluation, benchmarking and continuous deployment across edge and cloud platforms. 4. Collaborate with platform engineers to optimise models for latency and throughput using TensorRT, OpenVINO, DeepStream or ONNX Runtime. 5. Create complex event‑processing logic that correlates detections, applies temporal/spatial rules and filters false positives. 6. Develop tooling for dataset management, including automated annotation, quality checks, synthetic data generation and active‑learning loops. 7. Diagnose and resolve performance issues arising from varied lighting, occlusion or camera angles, applying algorithmic or data‑centric fixes. 8. Document pipeline architecture, model interfaces, performance benchmarks and deployment guides for internal and external stakeholders. Tech Stack: C++ / Python, OpenCV, NumPy, PyTorch or TensorFlow, TensorRT/OpenVINO, ONNX Runtime, Git, Docker, CI/CD tools, video codecs (H.264/H.265), streaming protocols (RTSP, HLS). Growth Path: Successful trainees can progress to Senior Vision Engineer, Lead AI Solutions Architect, or move into product management roles within HCLTech’s AI & Automation practice. The company offers regular up‑skilling programs, certifications and the chance to work on high‑impact projects for Fortune‑500 clients. Why Join HCLTech? The firm provides a vibrant, inclusive environment where fresh talent is mentored by industry veterans. Competitive compensation, exposure to cutting‑edge AI technologies, and a clear roadmap for career advancement make it an ideal launchpad for aspiring computer‑vision professionals.