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

About the Role
We are looking for a Data Scientist / AI Engineer / Generative AI Engineer to build and deploy intelligent AI applications using NLP, LLMs, RAG, and Machine Learning for financial-services use cases.
Key Responsibilities
Build AI/GenAI applications using NLP, LLMs, and Deep Learning.
Design and optimize RAG, semantic search, hybrid search, and vector search solutions.
Integrate LLMs/SLMs for Q&A, summarization, document understanding, and search.
Develop production-grade REST APIs using Python, FastAPI, Flask, etc.
Apply Prompt Engineering and Generative AI techniques.
Work with Elasticsearch, FAISS, Pinecone, Weaviate and similar technologies.
Develop NLP/ML models for classification, NER, embeddings, sentiment analysis, etc.
Monitor, evaluate, and improve LLM performance, accuracy, relevance, and latency.
Contribute to LLMOps/MLOps and production AI deployments.
Work independently with cross-functional teams to solve business and technical problems.
Requirements
Mandatory Requirements
3+ years of hands-on experience in Data Science / AI / ML / Deep Learning / NLP / GenAI.
Strong Python, backend, API development, and production support experience.
Hands-on experience with PyTorch / TensorFlow / Keras / Scikit-learn.
Strong NLP experience across text classification, NER, semantic search, embeddings, or document understanding.
Hands-on experience with LLMs such as GPT, LLaMA, Mistral, Phi, Claude, Gemini, or similar.
Proven experience building RAG / Vector Search / Semantic Search solutions.
Strong Prompt Engineering experience with LangChain, LangGraph, AI Agents, Azure OpenAI, or similar.
Experience developing/integrating APIs using FastAPI, Flask, or similar.
B.Tech/M.Tech from Tier-1 institutes – IITs, NITs, BITS.
Age: Below 28 years.
CTC structure: 75% Fixed + 25% Variable.
Good to Have
LLMOps/MLOps experience.
Azure OpenAI, AKS, Kubernetes, Docker and cloud-native AI deployments.
Experience with PostgreSQL, MongoDB, Redis, Kafka or large-scale data platforms.
Background in AI-first startups, product/SaaS companies, fintech, or data-driven technology companies.

Work arrangement
No