ABOUT US:

Technosoft Engineering is building an AI-powered visual intelligence platform. We design and deploy end-to-end Vision AI solutions that transform video and image data into actionable business intelligence.

THE ROLE:

We are looking for a Vision AI Architect to lead the design and implementation of large-scale Vision AI platforms and solutions. The role requires deep expertise across the entire video and vision lifecycle: capture, encoding, transcoding, transport, preprocessing, model development, training, deployment, scaling, application integration, and production operations. You will define reference architectures, guide engineering teams, and lead customer engagements from solution discovery through deployment.

Video Systems, Media Processing & Streaming Architecture:

- Deep expertise in video capture, ingestion, encoding, transcoding, packaging, streaming, storage, and distribution.

- Strong understanding of video codecs and standards: H.264, H.265/HEVC, AV1, MPEG-TS, MP4, RTSP, RTP, WebRTC, SRT, HLS, DASH.

- Hands-on experience designing scalable video pipelines using FFmpeg, GStreamer, NVIDIA DeepStream, or equivalent frameworks.

- Experience with edge-to-cloud video transportation architectures and low-latency streaming systems.

- Knowledge of video quality optimization, bitrate adaptation, frame extraction, synchronization, and metadata management.

- Experience handling large-scale video workloads across distributed environments.

Vision AI Platform Architecture:

- Design and implementation of end-to-end Vision AI platforms.

- Architecture experience covering ingestion, preprocessing, annotation, model training, inference, monitoring, and feedback loops.

- Hands-on experience with object detection, segmentation, tracking, OCR, anomaly detection, and multimodal AI systems.

- Expertise in YOLO, SAM, Vision Transformers, CNNs, and modern foundation vision models.

- Ability to select and define scalable AI architectures based on business requirements.

Model Development, Training & Optimization:

- Experience building and training production-grade vision models on custom datasets.

- Deep understanding of dataset design, annotation strategies, augmentation, transfer learning, and active learning.

- Model optimization using TensorRT, ONNX Runtime, quantization, pruning, and GPU acceleration.

- Experience deploying models across edge, cloud, and hybrid environments.

Production AI & MLOps Architecture:

- Define enterprise-grade MLOps architectures using MLflow, Weights & Biases, ClearML, Kubeflow, or equivalent.

- Design automated CI/CD pipelines for model deployment, validation, rollback, and monitoring.

- Experience with Kubernetes-based AI platforms and GPU orchestration.

- Establish model governance, drift monitoring, performance tracking, and observability.

End-to-End Vision Solution Delivery:

- Lead complete solution lifecycle from camera selection and placement through AI deployment and business application integration.

- Experience integrating Vision AI with enterprise applications, dashboards, workflows, APIs, and business systems.

- Strong understanding of presentation layers, visualization platforms, and real-time operational dashboards.

- Ability to translate customer requirements into scalable solution architectures.

Cloud, Edge & Distributed Systems:

- AWS, Azure, or GCP architecture experience for AI and video workloads.

- Edge AI deployment using NVIDIA Jetson, Intel OpenVINO, or equivalent platforms.

- Distributed data processing using Kafka, Spark, RabbitMQ, or similar technologies.

Leadership & Customer Engagement:

- Lead architecture reviews, technical governance, and solution roadmaps.

- Mentor engineering teams and establish architecture standards.

- Support pre-sales, solution workshops, customer discovery sessions, and implementation planning.

Programming Languages & Technology Stack:

- Expert-level Python, C/C++ or similar core programming languages.

- Strong experience with PyTorch, TensorFlow, OpenCV, CUDA, NVIDIA ecosystem, and AI acceleration frameworks.

- Understanding of microservices, REST APIs, containerization, and distributed systems.

QUALIFICATIONS:

- B.Tech / M.Tech in Computer Science, Electronics, AI/ML, Computer Vision, or related engineering discipline.

- 1015+ years of professional software engineering and solution architecture experience.

- 7+ years delivering production Vision AI, video analytics, or media AI solutions.

- Proven experience leading end-to-end Vision AI engagements and large-scale deployments.