AI Engineer
Generative AI engineer at Dentsu Webchutney/Merkle building agentic, multimodal, and knowledge-grounded AI applications that power customer engagement for global brands. Day to day involves LLM fine-tuning, RAG/retrieval systems, agents (LangChain/LangGraph), and production deployment with Python, FastAPI, Docker, and AWS/Azure/GCP.
- Compensation
- Not disclosed
- Location
- Pune
- Work setup
- Onsite
- Seniority
- Not specified
- Employment
- Full time
- Experience
- 3+ years
- Category
- Ai engineering
- Posted
Skills
Must have
- Minimum 3-4 years hands-on software development experience including building and deploying ML models into production
- 2+ years experience with deep learning, GenAI, or transformer-based architectures
- Demonstrated experience building GenAI applications beyond simple RAG (agents, multimodal, custom LLM fine-tuning)
- Experience integrating AI systems in enterprise-grade environments
- Advanced Python and SQL; API development and data engineering experience
- Backend frameworks: Flask, FastAPI, Django
- ML skills: 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; data pipelines and large-scale processing
- Math foundations: linear algebra, probability, statistics
- Open to hybrid work with no less than 2 days per week work from office
- Strong problem-solving skills and ability to communicate complex concepts to non-technical stakeholders
Full description
AI Engineer Role Overview
We are seeking a 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 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.