About this role
Job Description: AI Lead Engineer
Role Overview We are seeking a Lead Generative AI Engineer with strong foundations in deep learning, transformer architecture, and practical experience building GenAI applications beyond basic RAG systems. The ideal candidate has hands-on experience/technical familiarity with LLM fine-tuning, multimodal models, retrieval systems, agentic frameworks, retrieval architectures, and production-grade ML deployment. This role will partner with engineering, data science, and CX teams to build intelligent agents, multimodal experiences, personalization systems, and knowledge-grounded AI solutions that power the future of customer engagement for global brands.
Key Responsibilities Generative AI, Multimodal Systems & Agentic Frameworks
• Build conversational and non-conversational, multimodal, and agentic AI applications using LLMs and frameworks such as LangChain, LangGraph, LlamaIndex, AutoGen, or similar. • Design AI workflows incorporating reasoning, planning, tool-use, memory, grounding, and external system integrations. • Develop Knowledge Graph (KG)-assisted AI systems, including entity extraction, linking, and KG-augmented retrieval. • Ensure safety, consistency, and hallucination-control through structured evaluation and guardrails. Deployment, APIs & Cloud Engineering
• 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. Model Development & Applied AI Engineering
• Build and optimize transformer-based and multimodal models using deep learning frameworks (e.g., PyTorch, TensorFlow). • 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. Collaboration, Documentation & Mentorship
• 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. Required Technical Skills
• Programming: Python (advanced), SQL; robust experience with API development and data engineering, • Backend Frameworks: Flask, FASTAPI, Django • Machine Learning: Predictive modelling, deep learning, optimization, embeddings, vector search, model evaluation. • Generative AI: LLMs, RAG, multimodal architectures, agents, prompt engineering, grounding, knowledge graphs. • Cloud Platforms: AWS, Azure, or GCP with hands-on experience deploying and scaling AI systems. • Data Technologies: Apache Spark, Hadoop, MongoDB; strong understanding of data pipelines and large-scale processing. • Math Foundations: Linear algebra, probability, statistics. Experience 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.
Skill Category Lead AI Engineer Transformers & Deep Learning Applies LoRA/QLoRA, distillation, debugging, optimization. Generative AI (LLMs & Multimodal) Builds tool-using pipelines, multilingual/multimodal flows. Information Retrieval & Relevance Implements hybrid retrieval + ranking, KG-enhanced semantic retrieval Predictive Modeling Builds and tunes end-to-end ML pipelines. Knowledge Graphs Builds KG pipelines (entity linking, embeddings). Conversational AI Multi-turn, multilingual dialogue systems with evaluation metrics. Agentic Frameworks Multi-step agent workflows with planning & memory. Model Deployment Scales services with CI/CD, monitoring, GPU/accelerator ops. Cloud & MLOps End-to-end model lifecycle automation. Big Data & Pipelines Uses Spark/Hadoop/MongoDB effectively. Deep Learning Understand and applied deep learning architectures – RNNs, LSTMs, Transformers
Attitude & Mindset
• Growth-oriented, collaborative, and experimentation-driven. • Strong problem-solving skills with a bias toward action. • Ability to communicate complex concepts clearly to non-technical stakeholders. • Open and flexible towards a hybrid work structure with no less than 2-days work from office – This is to ensure that the team working in the AI domain regularly connects and does knowledge exchange across projects
Location: DGS India - Pune - Kharadi EON Free Zone Brand: Merkle Time Type: Full time Contract Type: Permanent