Lead AI Engineer

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.

Requirements

• Minimum 5+ 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.

Job Description

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.

Must Have

Demonstrated experience building GenAI applications beyond simple RAG (e.g., agents, multimodal, custom LLM fine-tuning). Experience integrating AI systems in enterprise-grade environments.

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.

Location

DGS India - Bengaluru - Bhartiya City Block 1 (inactive) Brand: Merkle Time Type: Full time Contract Type: Permanent