Job Description & Summary
We are looking for a Vision AI Engineer to deliver production-grade computer-vision solutions for industrial environments. In addition to model development, the role covers the image-acquisition system, including cameras, optics, lighting, positioning, triggering and calibration, so that models receive consistent and usable visual data.
A career within Enterprise Architecture services, will provide you with the opportunity to bring our clients a competitive advantage through defining their technology objectives, assessing solution options, and devising architectural solutions that help them achieve both strategic goals and meet operational requirements. We help build software and design data platforms, manage large volumes of client data, develop compliance procedures for data management, and continually researching new technologies to drive innovation and sustainable change.
Job Position Title
IN_Sr Associate_Vision AI Engineer (Industrial Digital & AI)_AppTech_Advisory_Mumbai (Early joiners)
Responsibilities
Key Responsibilities
- Assess inspection, detection,
safety or traceability requirements and translate them into measurable computer-vision acceptance criteria.
- Design image-acquisition setups
covering camera type, lens, field of view, resolution, frame rate, lighting, mounting, triggering and calibration.
- Plan and execute data collection;
define annotation guidelines and manage image/video dataset quality, coverage, versioning and governance.
classification, object-detection, segmentation, tracking, OCR or anomaly-detection models as required.
- Optimize models for accuracy,
latency, throughput and resource consumption on edge or cloud targets.
- Build preprocessing, inference and
post-processing pipelines and integrate results with Digital Twin, MES, quality and workflow systems.
- Define testing across products,
shifts, lighting conditions, camera drift, occlusions and realistic failure modes.
- Implement model and system
monitoring, dataset feedback loops, retraining triggers and controlled release practices.
- Support site installation,
commissioning, root-cause analysis and performance stabilization.
- Document hardware settings,
calibration, model versions, test evidence and operating procedures.
Expected Delivery Outcomes
- Repeatable image acquisition and a governed,
representative dataset.
- Validated vision model meeting agreed accuracy, latency
and throughput criteria.
- Deployable edge/cloud pipeline with monitoring,
calibration and support documentation.
Working Style and Stakeholder Engagement
- Work as part of a cross-functional delivery POD with
manufacturing, engineering, data, cloud, enterprise application and change teams.
- Communicate complex technical topics in clear business
language and maintain disciplined documentation, risk tracking and knowledge transfer.
- Support client workshops, design reviews, testing,
deployment and adoption activities; travel to manufacturing locations when required.
Mandatory skill sets
4-8 years of experience in computer vision, machine learning or image-processing solutions, preferably in production environments.
Strong Python and practical experience with OpenCV and deep-learning frameworks such as PyTorch or TensorFlow.
Hands-on experience with detection, segmentation, classification, tracking, OCR and image-quality assessment.
Understanding of industrial cameras, lenses, lighting, calibration, triggering, networking and edge-compute constraints.
Experience curating and annotating datasets, selecting metrics and evaluating false-accept/false-reject trade-offs.
Experience deploying optimized inference pipelines using containers, APIs and edge accelerators.
Ability to troubleshoot the complete vision chain from image formation to model output and system action.
Bachelor’s or master’s degree in computer science, electronics, instrumentation, robotics or a related discipline.
Preferred skill sets
Experience with visual quality inspection, defect detection, PPE/safety monitoring, identification, counting or dimensional checks.
Exposure to NVIDIA Jetson, TensorRT, ONNX, industrial vision SDKs or equivalent edge technologies.
Knowledge of MLOps, active learning, synthetic data and Digital Twin integration.
Experience operating vision systems under variable plant conditions and production-line cycle-time constraints.
Years of experience required:
4-8 years
Education qualification
B.E./ B.Tech / MCA/ M.E/ M.TECH/ MBA/ 2 yrs PGDM.. All qualifications should be in regular full-time mode with no extension of course duration due to backlogs
