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

About the Role

We are looking for a strong AI Engineer / Data Scientist / Generative AI Engineer to build intelligent AI-powered applications for the financial services domain. The role involves working across NLP, LLMs, RAG, semantic search, machine learning, and production-grade AI application development.

The ideal candidate should be a strong hands-on individual contributor with excellent Python and backend development skills and experience taking AI solutions from development to production.

Key Responsibilities

• Design and develop intelligent AI/ML and Generative AI applications using NLP and LLM techniques to solve real-world business problems.
• Build and optimize Retrieval-Augmented Generation (RAG) pipelines using structured and unstructured data.
• Integrate and orchestrate LLMs/SLMs for question answering, summarization, semantic search, and document understanding.
• Develop and maintain RESTful APIs, including synchronous and asynchronous APIs, using FastAPI, Flask, or similar frameworks.
• Apply advanced prompt engineering and prompting techniques to improve LLM performance.
• Build semantic search, hybrid search, and text retrieval systems using Elasticsearch and vector databases such as FAISS, Pinecone, or Weaviate.
• Develop NLP solutions involving NER, text classification, intent detection, embeddings, sentiment analysis, and document understanding.
• Build, train, and evaluate Machine Learning and Deep Learning models for NLP and structured data use cases.
• Monitor, evaluate, and optimize LLM/SLM performance using real-world data to improve accuracy, relevance, latency, and reliability.
• Work with LLMOps/MLOps tools for model monitoring, evaluation, experimentation, and versioning.
• Develop traditional ML models for structured data analysis and predictive use cases.
• Collaborate with cross-functional teams to identify system issues, business opportunities, and areas for improvement.
• Participate in solution design and implementation across new and existing projects.
• Work independently as an Individual Contributor, taking ownership of assigned projects and deliverables.

Mandatory Requirements

• 3+ years of hands-on experience in Data Science, AI, ML, Deep Learning, NLP, or Generative AI application development.
• Strong hands-on experience in Python programming, backend development, API development, and production-grade application support.
• Experience with ML/DL frameworks such as PyTorch, TensorFlow, Keras, or Scikit-learn.
• Hands-on experience with NLP use cases such as text classification, sentiment analysis, NER, semantic search, embeddings, or document understanding.
• Strong experience working with LLMs such as GPT, LLaMA, Mistral, Phi, Claude, Gemini, or similar models.
• Hands-on experience building or implementing RAG solutions, vector search, semantic search, or knowledge-based AI applications.
• Strong experience in Prompt Engineering and Generative AI frameworks/technologies such as LangChain, LangGraph, AI Agents, Azure OpenAI, or similar.
• Experience developing, consuming, or integrating APIs using FastAPI, Flask, or similar Python frameworks.
• Strong ability to work as an independent contributor with a problem-solving mindset.
• Age: Candidate should be below 28 years.
• Education: B.Tech/M.Tech from Tier 1 institutions, including IITs, NITs, BITS, IIITs, DTU, or NSUT.

Preferred Qualifications

• Experience with LLMOps/MLOps tools for monitoring, evaluation, experimentation, and model versioning.
• Exposure to Azure OpenAI, Azure Kubernetes Service (AKS), Kubernetes, cloud-native AI deployments, or distributed systems.
• Experience with PostgreSQL, MongoDB, Redis, Kafka, or large-scale data platforms.
• Familiarity with Docker, Kubernetes, cloud platforms, and scalable deployment architectures.
• Experience working with AI-first startups, product companies, SaaS organizations, fintech, or data-driven technology companies.

Compensation

The compensation structure will be 75% Fixed + 25% Variable, as per company policy.

Ideal Candidate

The ideal candidate is a hands-on AI/ML engineer with strong Python, NLP, LLM, RAG, and backend development expertise, who can independently build and deploy production-grade Generative AI solutions and contribute across the complete AI application lifecycle.