AI Engineer

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