Lead Engineer

Lead Engineer - AI

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Experience:

Role Type:

About the Role

Lead Engineer

Key Responsibilities

• End-to-End AI Delivery:

  Drive the entire lifecycle of AI solutions – requirement analysis, data preparation, model development, optimization, deployment, and monitoring in production environments.

• Computer Vision Solutions:

• Develop models for object detection, recognition, tracking, OCR, and video analytics.
• Optimize models for real-time performance (GPU/edge devices such as NVIDIA Jetson).
• Ensure robustness in challenging conditions (e.g., occlusion, lighting, class imbalance).

• Generative AI Applications:

• Integrate LLMs and multimodal AI for use cases like video summarization, natural language queries, automated insights, and incident reporting.
• Build practical workflows combining CV and GenAI (e.g., retrieval-augmented search across video data).

• Technical Leadership:

• Provide architectural guidance for AI pipelines (data ingestion → model inference → post-processing → serving).
• Establish coding standards, review best practices, and mentor junior engineers.
• Drive MLOps practices (CI/CD, model monitoring, drift detection, retraining strategies).

• Team Management:

• Lead and mentor a team of 8–10 engineers, fostering growth and accountability.
• Collaborate with product managers and stakeholders to translate business problems into measurable AI solutions.
• Ensure timely delivery with high technical quality.

• Innovation & Quality:

• Stay updated with emerging tools in CV and GenAI, evaluate applicability, and introduce best practices.
• Focus on scalability, cost optimization, and practical deployment challenges.

Required Qualifications

• Education:

  B.E./B.Tech/MCA in Computer Science, IT, or related field.

• Experience:

  5–10 years in AI/ML with preferably 2 years in a team lead role.

• Technical Expertise:

• Computer Vision:

  Proficiency in frameworks like PyTorch, TensorFlow, and OpenCV. Experience with object detection (YOLO, Faster R-CNN), segmentation, OCR, or tracking algorithms.

• Generative AI:

  Exposure to LLM-based solutions (LangChain, RAG pipelines, or similar frameworks). Ability to integrate GenAI into CV workflows.

• Deployment & MLOps:

  Hands-on with APIs (FastAPI/Flask), containerization (Docker), orchestration (Kubernetes), model registries, and monitoring tools.

• Performance Optimization:

  Familiarity with GPU acceleration (CUDA, TensorRT, ONNX Runtime) and scaling inference for production workloads.

• Programming:

  Strong Python expertise; exposure to C++/CUDA is a plus.

• Leadership Skills:

• Proven record of leading 6–10 member engineering teams.
• Ability to balance technical depth with delivery timelines.
• Strong communication and problem-solving skills.



Why Join Us?

• Opportunity to

  lead impactful AI projects

  combining Computer Vision and Generative AI.
• Work with a

  talented engineering team

  in a high-growth environment.
• Exposure to

  cutting-edge technology

  while solving real-world problems at scale.