Job Description:AI Lead EngineerRole 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 ResponsibilitiesGenerative AI, Multimodal Systems & Agentic FrameworksBuild 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 EngineeringTransform 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 EngineeringBuild 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 & MentorshipWork 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 SkillsProgramming: Python (advanced), SQL; robust experience with API development and data engineering,Backend Frameworks: Flask, FASTAPI, DjangoMachine 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 RequirementsMinimum 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 CategoryLead AI EngineerTransformers & Deep LearningApplies LoRA/QLoRA, distillation, debugging, optimization.Generative AI (LLMs & Multimodal)Builds tool-using pipelines, multilingual/multimodal flows.Information Retrieval & RelevanceImplements hybrid retrieval + ranking, KG-enhanced semantic retrievalPredictive ModelingBuilds and tunes end-to-end ML pipelines.Knowledge GraphsBuilds KG pipelines (entity linking, embeddings).Conversational AIMulti-turn, multilingual dialogue systems with evaluation metrics.Agentic FrameworksMulti-step agent workflows with planning & memory.Model DeploymentScales services with CI/CD, monitoring, GPU/accelerator ops.Cloud & MLOpsEnd-to-end model lifecycle automation.Big Data & PipelinesUses Spark/Hadoop/MongoDB effectively.Deep LearningUnderstand and applied deep learning architectures – RNNs, LSTMs, TransformersAttitude & MindsetGrowth-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 projectsLocation:DGS India - Pune - Baner M- AgileBrand:MerkleTime Type:Full timeContract Type:Permanent