Lead AI Engineer

What you'll do

• Build conversational and non-conversational, multimodal, and agentic AI applications using LLMs and frameworks such as LangChain, LangGraph, LlamaIndex, AutoGen
• Design AI workflows incorporating reasoning, planning, tool-use, memory, grounding, and external system integrations
• Develop Knowledge Graph-assisted AI systems including entity extraction, linking, and KG-augmented retrieval
• Ensure safety, consistency, and hallucination-control through structured evaluation and guardrails
• Transform models into scalable APIs and microservices using Python, FastAPI/Flask, Docker
• Deploy and monitor ML/AI systems in AWS/Azure/GCP optimizing for cost, latency, and reliability
• Collaborate with MLOps teams on CI/CD pipelines, model versioning, monitoring, and automated evaluation
• Work with big data technologies including Apache Spark, Hadoop, and NoSQL databases such as MongoDB
• Build and optimize transformer-based and multimodal models using deep learning frameworks
• Implement fine-tuning, alignment (RLHF/RLAIF), LoRA/QLoRA, pruning, and model evaluation pipelines
• Develop information retrieval systems including hybrid dense–sparse retrieval, ranking, knowledge graphs, and relevance optimization
• Build predictive models and ML pipelines from scratch including data preparation, feature engineering, and model selection
• Work cross-functionally with CX, engineering, and product stakeholders to translate business needs into AI solutions
• Document models, experiments, evaluation frameworks, and deployment processes
• Mentor junior engineers and contribute to internal best practices, reusable components, and R&D initiatives

Requirements

• Minimum 5-6 years of hands-on software development experience including building and deploying machine learning models into production
• 2+ years of experience working with deep learning, GenAI, or transformer-based architectures
• Demonstrated experience building GenAI applications beyond simple RAG (e.g., agents, multimodal, custom LLM fine-tuning)
• Experience integrating AI systems in enterprise-grade environments
• Advanced Python programming skills
• Experience with API development and data engineering
• Experience with backend frameworks such as Flask, FastAPI, Django
• Knowledge of predictive modeling, deep learning, optimization, embeddings, vector search, model evaluation
• Hands-on experience with cloud platforms AWS, Azure, or GCP for deploying and scaling AI systems
• Experience with big data technologies including Apache Spark, Hadoop, and MongoDB
• Strong understanding of data pipelines and large-scale processing
• Mathematical foundations in linear algebra, probability, and statistics
• Growth-oriented, collaborative, experimentation-driven mindset
• Strong problem-solving skills with bias toward action
• Ability to communicate complex concepts clearly to non-technical stakeholders
• Open and flexible towards hybrid work structure with minimum 2 days in office

Tech stack