We are seeking an experienced Team Lead – AI to lead the design, development, and deployment of cutting-edge AI solutions across enterprise products. The ideal candidate will have strong expertise in Deep Learning, Large Language Models (LLMs), Computer Vision, and NLP, along with proven leadership experience in building and managing high-performing AI teams.
This role involves transforming traditional software systems into AI-driven intelligent platforms, driving innovation across multimodal AI, automation, and large-scale data systems.
Key Responsibilities
1. Technical Leadership
Lead the end-to-end development of AI solutions: from data collection → model development → deployment.
Architect scalable AI systems using LLMs, deep learning, and multimodal models.
Design and implement production-grade ML pipelines and AI-driven product features.
Guide the team in selecting appropriate models, architectures, and training strategies.
2. LLM & Generative AI Systems
Design and deploy LLM-based applications (RAG pipelines, chatbots, agents).
Work with open-source and proprietary models (e.g., LLaMA, Mistral, Qwen, etc.).
Implement:
Retrieval-Augmented Generation (RAG)
Prompt engineering & fine-tuning
Token streaming and real-time inference systems
Optimize models for latency, cost, and performance in production.
3. Computer Vision & Multimodal AI
Develop and deploy computer vision models for detection, recognition, and document understanding.
Build multimodal pipelines combining text, image, and audio inputs.
Work with OCR systems and document intelligence workflows.
4. Data Engineering & Model Training
Handle large-scale datasets (structured & unstructured).
Design data pipelines for:
ETL processes
Feature engineering
Dataset curation and annotation
Train models using best practices:
Distributed training
Mixed precision (FP16)
Efficient fine-tuning techniques
5. System Architecture & Deployment
Build scalable backend systems using:
FastAPI / Python frameworks
Docker-based deployments
GPU-based inference systems
Work with:
Databases (SQL/NoSQL)
Vector databases (FAISS, etc.)
Message queues & async processing (Celery, Redis)
6. Product Development & Innovation
Collaborate with product and business teams to:
Identify AI use cases
Translate requirements into AI solutions
Drive innovation in:
Automation platforms
Intelligent document processing
Predictive analytics systems
7. Team Leadership & Mentorship
Lead and mentor a team of AI engineers, data scientists, and interns.
Conduct technical reviews and ensure code/model quality.
Train junior engineers in:
AI/ML fundamentals
Python programming
Industry best practices
Manage task allocation, timelines, and delivery.
8. Research & Continuous Learning
Stay updated with state-of-the-art research in AI/ML.
Evaluate and integrate new models, tools, and frameworks.
Encourage research-driven development and experimentation.