AI Engineer
Posted Updated
Summary
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.
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 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 3-4 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
AI Engineer
Transformers & Deep Learning
Understands transformers; fine-tunes small models.
Generative AI (LLMs & Multimodal)
Works with LLM APIs; simple RAG and KAG - Demonstrated by project experience
Information Retrieval & Relevance
Uses vector DBs for retrieval, knowledge graphs - - Demonstrated by project experience
Predictive Modeling
Builds and deploys ML models; applies evaluation metrics - Demonstrated by project experience
Knowledge Graphs
Integrates and retrieves from existing KGs
Conversational AI
Text-only chatbots; intent models.
Agentic Frameworks
Basic agent/tool calling.
Model Deployment
Deploys models as basic APIs.
Cloud & MLOps
Uses cloud AI services.
Big Data & Pipelines
Writes SQL/Python ETL.
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 - Indiqube Orchid Brand:
Merkle Time Type:
Full time Contract Type:
PermanentDisclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.
Skills
- Agentic AI
- AI
- API
- AutoGen
- AWS
- Azure
- Big Data
- CI/CD
- Cloud
- Conversational AI
- Data Engineering
- Data Pipelines
- Data Science
- Deep Learning
- Django
- Docker
- Embeddings
- ETL
- FastAPI
- Feature Engineering
- Fine Tuning
- Flask
- GCP
- Generative AI
- Hadoop
- LangChain
- LangGraph
- LlamaIndex
- LLM
- Machine Learning
- Microservices
- MLOps
- Model Deployment
- Model Evaluation
- MongoDB
- NoSQL
- Predictive Modeling
- Prompt Engineering
- Python
- PyTorch
- RAG
- RLHF
- Spark
- SQL
- Statistics
- TensorFlow
- Transformers
- Vector Search