AI Engineer

• 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.

• 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.

Understands transformers; fine-tunes small models.

• 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.

• 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.

• 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.

Works with LLM APIs; simple RAG and KAG – Demonstrated by project experience

Uses vector DBs for retrieval, knowledge graphs - – Demonstrated by project experience

Builds and deploys ML models; applies evaluation metrics – Demonstrated by project experience

Integrates and retrieves from existing KGs

Text-only chatbots; intent models.

Basic agent/tool calling.

Deploys models as basic APIs.

Uses cloud AI services.

Writes SQL/Python ETL.

Understand and applied deep learning architectures – RNNs, LSTMs, Transformers

• 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

DGS India - Pune - Indiqube Orchid

Merkle

Full time

Permanent

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.