Senior Applied AI/ML Engineer – Computer Vision & Video

Objectways; Chennai District; On-site; INR 3,500,000 - 7,000,000; Full time

Job summary

Objectways is seeking a Senior Applied AI/ML Engineer with strong Computer Vision and video ML expertise to lead production-grade AI solutions for video understanding and multimodal tasks. You will drive end-to-end model development from data strategy through deployment.

Ideal candidates have 4–8+ years of hands-on AI/ML experience in CV/video, with a track record of end-to-end model ownership and deployment in real systems.

Qualifications

• Strong Python and PyTorch with hands-on model training, fine-tuning and experiment design.
• Solid CV experience with real model development across detection, segmentation, tracking or video understanding.
• Hands-on data processing with OpenCV/FFmpeg; data prep and quality handling.
• Ability to design proper training/validation/test splits, leakage prevention and benchmarking.
• Experience deploying models beyond notebooks using Docker and API serving (FastAPI).
• Ability to compare multiple technical approaches and justify architecture decisions.

Responsibilities

• Own technical design of applied AI/ML solutions with CV/video components.
• Build, fine-tune and benchmark models for detection, segmentation, tracking, and video understanding.
• Design datasets and evaluation protocols; perform failure analysis across video conditions.
• Evaluate modern vision/video/multimodal models and select appropriate approaches.
• Optimize GPU inference and integrate models into production services with backend/MLOps teams.

Skills

Python

PyTorch

Computer Vision

Experiment design

Model evaluation

Debugging

Team mentorship

Education

B.Tech / M.Tech / MS / PhD in Computer Science or related technical discipline

Tools

OpenCV

FFmpeg

Docker

FastAPI

GPU deployment tools (TensorRT, CUDA)

ONNX

Job description

We are looking for a senior Applied AI/ML Engineer with strong hands-on experience in Computer Vision and video systems to help lead the development of production-grade AI solutions for video understanding, detection/segmentation, tracking, multimodal reasoning and model-assisted annotation. This is a practical research-and-engineering role: the person should be comfortable taking a problem from data and experimentation through model training, evaluation, optimization and deployment. Prior Physical AI or robotics experience is not mandatory, but the candidate must have enough depth in vision/video ML to grow into those areas.

What you will own

• Own or co-own the technical design of applied AI/ML solutions with a strong Computer Vision and video component, from problem definition and data strategy through deployment.
• Build, fine-tune and benchmark models for areas such as object detection, instance/semantic segmentation, tracking, video understanding, action recognition or other temporal vision tasks.
• Design reliable datasets, annotation schemas, gold sets and leakage-safe evaluation protocols, and perform systematic failure analysis across real-world video conditions.
• Evaluate modern vision, video and multimodal models and decide when to use a general-purpose model, a specialist model, or a hybrid pipeline.
• Optimize GPU inference and integrate versioned models into production services, working with backend/MLOps and annotation-platform teams.

Profile:

We are open to experienced Applied AI/ML candidates whose strongest work is in Computer Vision, video ML or multimodal systems. The exact previous job title is not important; demonstrated end-to-end ownership of real model-development work is.

Relevant sourcing profiles / titles include: Senior Applied AI/ML Engineer; Senior AI/ML Engineer – Vision & Multimodal Systems; Senior Computer Vision Engineer; Machine Learning Engineer – Computer Vision; Video AI Engineer; Applied Scientist – Computer Vision; Multimodal AI Engineer; and Applied ML Research Engineer.

Typically 4–8+ years of relevant hands-on experience in applied AI/ML, with a meaningful portion of that work involving Computer Vision or video. Strong candidates with fewer years but clear end-to-end model ownership may also be considered.

B.Tech / M.Tech / MS / PhD in Computer Science, AI, Data Science, Electrical/ECE, Mathematics & Computing, Engineering Physics, Robotics, Mechatronics or a related technical discipline. Equivalent demonstrated industry or research depth is also acceptable.

Must Have:

• Strong Python and hands-on PyTorch experience, including model training, fine-tuning, debugging and experiment design.
• Solid practical experience in Computer Vision with real model-development work across at least two of the following: object detection, segmentation, tracking, pose estimation, video understanding, action recognition/localization or related temporal vision problems.
• Hands-on experience processing real image/video data using OpenCV, FFmpeg or equivalent tooling, including frame/clip preparation, augmentation and data-quality handling.
• Ability to design datasets and evaluation protocols correctly: train/validation/test construction, leakage prevention, class imbalance, hard negatives, gold-set creation and reproducible benchmarking.
• Strong model-evaluation and failure-analysis skills; able to reason about errors caused by motion blur, occlusion, domain shift, lighting, viewpoint changes, tracking drift or poor segmentation boundaries.
• Experience training or fine-tuning deep-learning models on GPUs, with practical understanding of VRAM, batching, mixed precision, throughput and inference trade-offs.
• Experience taking models beyond notebooks: Docker-based deployment and API/model serving using FastAPI or equivalent, with practical cloud/GPU deployment exposure.
• Ability to compare multiple technical approaches rather than simply integrate a single popular model, and to explain architecture decisions using measurable quality, latency and reliability trade-offs.
• Ability to provide technical guidance to junior engineers while remaining hands-on with code, experiments and debugging.

Good to Have:

• Multimodal/VLM or open-vocabulary vision experience, including vision-language grounding, video-language understanding or multimodal retrieval.
• Egocentric/first-person video, Physical/Embodied AI, hand-object interaction, manipulation data or annotation-automation experience.
• Experience with modern model families or equivalent approaches such as Grounding DINO, SAM-style models, Florence, Qwen-VL, InternVideo, ActionFormer, Mask2Former or contemporary detection/segmentation/video architectures.
• ONNX, TensorRT, Triton Inference Server, CUDA/performance profiling, distributed training or multi-GPU inference.
• Experience integrating model outputs into human-in-the-loop annotation platforms such as TensorAct, Encord, CVAT, Label Studio or equivalent.