Lead Engineer

Position Overview
We're hiring an
AI Lead Engineer
to architect, ship, and scale production-grade
Computer Vision
with a focused
GenAI
charter. You'll lead 6–10 ML/CV engineers to deliver high-accuracy, low-latency video analytics—detection, tracking, segmentation, recognition, re-identification—tackling multi-camera tracking, 24/7 streaming at scale, long-tail drift, and edge optimization on GPUs/Jetson, while partnering with Product/Platform to meet FPS, latency, accuracy, and cost SLAs. The day-to-day centers on core CV model design, training/leval, deployment, and streaming/edge performance, complemented by GenAI that amplifies the stack: integrating VLM/LLM capabilities for natural-language video search, incident summarization, operator Q&A, and "copilot" workflows; standing up RAG over video/sensor/metadata embeddings with robust prompts, tooling, evals, and guardrails; and driving data ops with synthetic data, auto-labeling, active-learning triage with privacy, safety, and cost controls.

What You'll Own

• End-to-end delivery of CV products: problem framing → data/labeling → model design → optimization → deployment → monitoring → iteration.
• Technical roadmap & architecture for video analytics pipelines (ingest → decode → infer → track → post-process → store/serve).
• Team leadership: mentoring, hiring input, OKRs, code/research standards, and performance coaching.

Key Responsibilities
Technical Leadership & Execution

• Translate business goals (e.g., "reduce shrinkage," "increase throughput," "improve safety") into measurable CV objectives and SLAs/SLOs (e.g., mAP/IDF1, per-frame latency, dropped-frame rate, cost/stream).
• Lead design reviews; establish MLOps and coding standards; enforce experiment tracking, reproducibility, and dataset/version governance.
• Drive capacity planning, GPU/Jetson utilization, batching/windowing strategy, autoscaling, and cost governance.

Computer Vision R&D (Detection, Tracking, Segmentation, Recognition)

• Deliver production models for object detection/segmentation/classification/tracking (e.g., YOLOv8/v9, Mask R-CNN/Mask2Former, EfficientNet/ConvNeXt, ByteTrack/OC-SORT/DeepSORT).
• Build person/vehicle/product re-ID, face/attribute recognition (e.g., ArcFace/CosFace), OCR (e.g., PP-OCR), and keypoint/action recognition (e.g., MMPose, SlowFast/X3D).
• Tackle domain adaptation, class imbalance, and occlusions; design augmentations and semi-supervised/active learning loops to harvest hard negatives.

Video Analytics & Edge Inference

• Architect real-time pipelines using NVIDIA DeepStream/GStreamer/OpenCV; optimize decode (NVDEC), pre/post, and trackers for 30–60 FPS at 1080p.
• Optimize with TensorRT/ONNX Runtime/Torch-TensorRT, INT8 calibration, pruning/distillation; leverage Jetson Orin/Xavier/Nano and DLA where applicable.
• Design multi-camera fusion, homography/camera calibration, and cross-camera ID consistency for retail, traffic, manufacturing, and security use cases.
• Implement privacy-by-design features (e.g., face/license blur, PII redaction).

Generative AI & LLMs

• Architect robust RAG: retrieval pipelines with Pinecone/ChromaDB/Milvus; index sharding/compaction; freshness policies; hybrid search.
• Design agents with LangChain/LangGraph; implement tool-use, safety filters, and guardrails; add evaluation loops (e.g., RAGAS/DeepEval-style).

Platform, Serving & MLOps

• Ship services via FastAPI/Flask; containerize with Docker; orchestrate on Kubernetes (KServe) or AWS SageMaker/Vertex AI.
• Build high-throughput inference with Triton Inference Server (dynamic batching, concurrent models, model ensembles).
• Streaming & storage: RTSP/RTMP ingest, Kafka/Kinesis, object storage + time-series DB; index/frame-level metadata for search and analytics.
• CI/CD with MLflow/DVC (artifacts, model registry), unit/integration tests, and rollout strategies (canary, shadow).

Observability, Drift & Governance

• Production monitoring with Prometheus/Grafana; per-stage latency, FPS, GPU memory/SM occupancy, dropped frames, and backpressure.
• Model observability: data/feature drift, concept drift on detections/tracks, re-ID distribution shifts, outlier/novelty detection, safety metrics.
• Human-in-the-loop review tools (CVAT/Label Studio) and auto-retraining triggers; maintain model cards, evaluation reports, versioned prompts/configs, and auditability.
• Ensure compliance and privacy/PII handling; ONVIF/edge security best practices.

Cross-Functional & People Leadership

• Partner with Product/SRE/DevOps on roadmaps, SLAs, incident response runbooks, and cost/perf tradeoffs.
• Lead and grow a 6–10 person CV team; foster a culture of high-quality experiments, rigorous reviews, and measurable impact.
• Communicate progress/risks to executives with clear, metric-driven updates and customer-facing results.

Required Qualifications

• Candidate shall have a degree in B.E/B.Tech/MCA in any discipline preferable computer science
• 5–8 years in ML/Computer Vision with 2+ years leading 6–10 engineers delivering production video analytics.
• Proven track record shipping detection/tracking/segmentation/recognition systems with business impact (accuracy, latency, cost).

Strong in at least one per category (and comfortable across most)
:

• CV Frameworks: PyTorch (preferred) or TensorFlow; OpenCV, NVIDIA DeepStream/GStreamer.
• Serving/Runtime: Triton Inference Server, FastAPI/Flask.
• Optimization: TensorRT, ONNX Runtime, Torch-TensorRT; quantization/pruning/distillation; INT8 calibration.
• Tracking/Re-ID/OCR: ByteTrack/OC-SORT/DeepSORT; ArcFace/CosFace; PP-OCR/Tesseract.
• Agents & Retrieval: LangChain or LangGraph; Pinecone/ChromaDB/Milvus
• MLOps: Docker, Kubernetes (KServe/SageMaker/Vertex AI), MLflow/DVC.
• Cloud: AWS (SageMaker, EC2/EKS) or GCP (Vertex AI) or Azure ML (AKS).
• Programming: Python (expert); C++ or Go for perf-critical components; CUDA fundamentals a plus.
• Streaming/IO: RTSP/RTMP, Kafka/Kinesis/Rabbitmq; ONVIF familiarity.
• Strong system design (multi-stream pipelines, GPU scheduling, distributed tracking/indexing) and excellent communication.

Preferred Qualifications

• Operating vector or time-series stores for video metadata at 10M–50M+ rows; search over tracks, IDs, and events.
• Experience with multi-camera tracking, calibration and zone-based analytics.
• Jetson fleet management.
• Ray/Ray Serve or Kubeflow; feature stores (Feast); complex event processing.
• Domain experience in one or more: retail analytics, traffic/ADAS, manufacturing QA, security/safety, sports analytics.
• Open-source contributions, patents, or publications in CV/video analytics.
• (Nice to have) Multimodal exposure—CLIP/SigLIP, SAM/Mask2Former, or VLMs for captioning/search—used sparingly to support CV workflows.

About Company
Hi there We are Auriga IT.

We power businesses across the globe through digital experiences, data and insights. From the apps we design to the platforms we engineer, we're driven by an ambition to create world-class digital solutions and make an impact. Our team has been part of building the solutions for the likes of Zomato, Yes Bank, Tata Motors, Amazon, Snapdeal, Ola, Practo, Vodafone, Meesho, Volkswagen, Droom and many more.

We are a group of people who just could not leave our college-life behind and the inception of Auriga was solely based on a desire to keep working together with friends and enjoying the extended college life.

Who Has not Dreamt of Working with Friends for a Lifetime

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