Data Scientist / AI Engineer (Python, ML, RAG) - GB04

Roles and Responsibilities

• Collaborate across functions to diagnose system challenges and coordinate resolution efforts with relevant technical teams.
• Develop production-ready RESTful API services (both synchronous and asynchronous) to expose NLP capabilities and conversational AI interfaces through frameworks such as FastAPI, Flask, and similar tools.
• Create and deploy text retrieval solutions, hybrid search systems, and semantic search capabilities leveraging Elasticsearch alongside vector databases including FAISS, Pinecone, and Weaviate.
• Continuously refine LLM and SLM outputs based on production user feedback to enhance accuracy, reduce response time, and improve relevance.
• Must demonstrate proficiency in sophisticated prompting techniques.
• Engineer and enhance Retrieval-Augmented Generation (RAG) workflows that utilize both structured and unstructured datasets from the financial domain.
• Maintain a positive, solutions-oriented mindset and function effectively as an independent contributor.
• Execute NLP operations including entity recognition, text classification, intent detection, embedding generation, and sentiment analysis as business needs dictate.
• Construct, train, and assess deep learning architectures for NLP applications such as classification, NER, summarization, and embedding generation.
• Analyze business operations to pinpoint enhancement opportunities and contribute to both identifying and deploying solutions.
• Build intelligent AI solutions powered by advanced NLP and LLM methodologies to address practical business problems within financial services.
• Operate autonomously as an Individual Contributor across both established and emerging projects.
• Work with LLMOps platforms to track, assess, and version AI models operating in live environments.
• Deploy and coordinate LLM and SLM systems for applications including question-answering, summarization, semantic search, and document understanding.
• Create traditional machine learning solutions such as regression, decision trees, and clustering for analyzing structured datasets and generating predictions.

Requirements

• Profile must be that of a Strong Data Scientist, AI Engineer, or Generative AI Engineer.
• Mandatory (Experience 1) - A minimum of 3 years of practical, hands-on experience in Data Science, Artificial Intelligence, Machine Learning, Deep Learning, NLP, or developing Generative AI applications is required.
• Mandatory (Experience 2) - Demonstrable hands-on expertise in Python programming, backend development, API development, and supporting production-grade applications is required.
• Mandatory (Experience 3) - Practical experience with Machine Learning and Deep Learning frameworks including PyTorch, TensorFlow, Keras, or Scikit-learn is required.
• Mandatory (Experience 4) - Hands-on experience delivering NLP use cases is required, including text classification, sentiment analysis, entity recognition (NER), semantic search, embeddings, or document understanding.
• Mandatory (Experience 5) - Practical experience working with Large Language Models (LLMs) is required, including GPT, LLaMA, Mistral, Phi, Claude, Gemini, or comparable models.
• Mandatory (Experience 6) - Required hands-on experience constructing or deploying Retrieval Augmented Generation (RAG) systems, vector search, semantic search, or knowledge-based AI applications.
• Mandatory (Experience 7) - Experience with Prompt Engineering and Generative AI frameworks is required, such as LangChain, LangGraph, AI Agents, Azure OpenAI, or comparable technologies.
• Mandatory (Experience 8) - Required experience in developing, consuming, or integrating API services using Python frameworks including FastAPI, Flask, or comparable technologies.
• Mandatory (CTC) - The compensation structure provided will follow company policy: 75% fixed and 25% variable CTC breakup.
• Preferred (Experience 1) - Experience utilizing LLMOps and MLOps tools for monitoring, evaluation, experimentation, and versioning of AI models.
• Preferred (Experience 2) - Exposure to Azure OpenAI, Azure Kubernetes Service (AKS), Kubernetes, cloud-native AI deployments, or distributed systems.
• Preferred (Experience 3) - Experience with PostgreSQL, MongoDB, Redis, Kafka, or large-scale data platforms.
• Preferred (Experience 4) - Familiarity with Docker, Kubernetes, cloud platforms, and scalable deployment architecture.
• Preferred (Company) - Candidates from AI-first startups, product companies, SaaS organizations, fintech, or data-driven technology companies.
• Mandatory ( Age ) - Candidate must be Below 28 Years.
• Mandatory (Education) - B.TECH or M.TECH from Tier 1 Colleges (IIT, NIT, BITS, IIIT, DTU, NSUT) are considered.

Experience: 3–6 years · Work from office · 5 days a week · Notice period up to 30 days

Benefits

At Bajaj Finance Limited, one of India's most diversified Non-banking financial companies and ranked among Asia's top 10 Large workplaces, our team members shape our vision and drive extraordinary accomplishments. For those with ambition and determination, we offer career opportunities spanning more than 500 locations across India.