AI Engineer

Overview

Generative AI Engineer role building intelligent agents, multimodal experiences, personalization systems, and knowledge-grounded AI solutions for customer engagement with global brands, partnering with engineering, data science, and CX teams.

What you'll do

• 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.
• 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.

What you'll need

• 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.
• Understanding of deep learning architectures: RNNs, LSTMs, Transformers.
• Attitude and mindset: growth-oriented, collaborative, experimentation-driven, strong problem-solving with a bias toward action, and ability to communicate complex concepts clearly to non-technical stakeholders.

Details

• Location: DGS India - Pune - Indiqube Orchid.
• Hybrid work structure with no less than 2 days work from office.
• Time type: Full time; contract type: Permanent.