Job 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 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:
Permanent