Job Purpose:
We are seeking a highly experienced Lead AI/ML Engineer to drive the design, development, and deployment of advanced AI solutions across the organization. The ideal candidate will have 6–10 years of experience in AI/ML, with strong expertise in Generative AI, Large Language Models (LLMs), Computer Vision, and AI platform architecture.
As a Lead AI/ML Engineer, you will define AI strategy, architect scalable AI systems, and lead a team of engineers and data scientists to build production-ready AI solutions. You will play a critical role in translating business problems into innovative AI-driven products, leveraging technologies such as LLMs, Retrieval-Augmented Generation (RAG), AI agents, and multimodal AI systems.
You will collaborate closely with Product, Engineering, and Data teams to build intelligent, scalable, and high-performance AI platforms that power next-generation applications.
Key Responsibilities:
AI Strategy & Technical Leadership
Lead the architecture, design, and implementation of enterprise-scale AI/ML solutions.
Define and drive the AI/ML roadmap, ensuring alignment with business objectives and product strategy.
Provide technical leadership and mentorship to AI/ML engineers and data scientists.
Establish best practices for AI model development, experimentation, deployment, and monitoring.
Generative AI & LLM Systems
Design and develop Generative AI applications using LLMs such as GPT, LLaMA, Gemini, or custom models.
Architect and implement Retrieval-Augmented Generation (RAG) pipelines for enterprise knowledge systems.
Lead initiatives for LLM fine-tuning, prompt engineering, and model optimization.
Design AI agent architectures using frameworks like LangChain, LangGraph, and LlamaIndex.
AI/ML Model Development:
Develop and deploy NLP, Computer Vision, and multimodal AI models for real-world business applications.
Implement advanced deep learning architectures using PyTorch, TensorFlow, or Keras.
Identify and evaluate pre-trained and foundation models suitable for specific use cases.
Drive data preprocessing, feature engineering, and dataset curation for model training.
AI Platform & InfrastructureDesign scalable AI infrastructure and MLOps pipelines for model training, deployment, and monitoring.
Deploy AI solutions across cloud platforms (AWS, Azure, GCP) or hybrid/on-premise environments.
Build APIs, microservices, and pipelines to integrate AI capabilities into enterprise applications.
Lead efforts in model optimization, inference acceleration, and resource efficiency.
Performance Optimization & Quality
Conduct model evaluation, benchmarking, and continuous performance optimization.
Optimize AI systems for latency, scalability, and cost efficiency.
Implement testing, monitoring, and observability frameworks for AI systems in production.
Collaboration & Innovation
Work closely with Product, Engineering, and Data teams to define AI-powered product features.
Stay at the forefront of AI research and emerging technologies, evaluating their business impact.
Promote a culture of experimentation, innovation, and knowledge sharing within the AI team.
Required Skills & Experience
AI & Machine Learning
6–10 years of experience in AI/ML development and deployment.
Strong expertise in supervised and unsupervised learning techniques, including regression, classification, clustering, SVMs, and neural networks.
Generative AI & LLMs
Hands-on experience with LLM training, fine-tuning, prompt engineering, and optimization.
Experience building GenAI applications such as chatbots, AI assistants, and document intelligence systems.
NLP & Computer Vision
Strong experience in Natural Language Processing and Computer Vision.
Hands-on expertise with Transformers, OpenCV, YOLO, and R-CNN architecture.
AI Agents & Frameworks
Experience with multi-agent frameworks such as LangChain, LangGraph, and LlamaIndex.
Deep Learning Frameworks
Proficiency in PyTorch, TensorFlow, or Keras.
Programming
Strong programming skills in Python with experience in API development and microservices.
Cloud & AI Infrastructure
Experience deploying AI models on AWS, Azure, or Google Cloud Platform.
Familiarity with MLOps pipelines, model serving, and AI lifecycle management.
Vector Databases
Hands-on experience with vector databases such as FAISS, Pinecone, ChromaDB, or Weaviate.
Performance Optimization
Experience optimizing LLM inference for speed, cost, and memory efficiency.
Leadership & Collaboration
Pay: ₹1,500,000.00 - ₹2,500,000.00 per year
Work Location: In person
