Seeking a Generative AI Engineer with expertise in deep learning, transformer architectures, and building GenAI applications beyond basic RAG systems. Responsibilities include developing multimodal and agentic AI applications using frameworks like LangChain and LlamaIndex, designing AI workflows with reasoning and external integrations, developing Knowledge Graph-assisted AI systems, ensuring safety and hallucination control, deploying scalable APIs and microservices with Python and Docker, monitoring ML systems on AWS/Azure/GCP, collaborating on CI/CD pipelines, working with big data technologies such as Apache Spark, Hadoop, and MongoDB, building and optimizing transformer-based models, implementing fine-tuning and model evaluation pipelines, developing information retrieval systems, building predictive models, collaborating cross-functionally, documenting processes, and mentoring junior engineers. Requires 3-4 years software development experience including ML model deployment, 2+ years with deep learning and GenAI, advanced Python and API development skills, experience with backend frameworks, cloud platforms, big data technologies, and strong math foundations. Hybrid work model with minimum 2 days in office in Pune, Maharashtra, India.
What you'll do
Similar jobs
More roles worth a look
Related opportunities based on specialty and working model so candidates can keep momentum.
Build conversational and non-conversational, multimodal, and agentic AI applications using LLMs and frameworks such as LangChain, LangGraph, LlamaIndex, AutoGen
Design AI workflows incorporating reasoning, planning, tool-use, memory, grounding, and external system integrations
Develop Knowledge Graph-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
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
Requirements
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, Generative AI, or transformer-based architectures
Experience building Generative AI applications beyond simple RAG (e.g., agents, multimodal, custom LLM fine-tuning)
Experience integrating AI systems in enterprise-grade environments
Advanced Python programming skills
Robust experience with API development and data engineering
Experience with backend frameworks such as Flask, FastAPI, Django
Knowledge of predictive modeling, deep learning, optimization, embeddings, vector search, model evaluation
Hands-on experience with cloud platforms AWS, Azure, or GCP for deploying and scaling AI systems
Strong understanding of data pipelines and large-scale processing using Apache Spark, Hadoop, MongoDB
Mathematical foundations in linear algebra, probability, and statistics
Ability to communicate complex concepts clearly to non-technical stakeholders
Open and flexible towards a hybrid work structure with minimum 2 days in office
