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 (inactive)
Brand:
Merkle
Time Type:
Full time
Contract Type:
Permanent#DGS