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

ApplyMint; Pune District; On-site; INR 1,200,000 - 2,500,000; Full time

Job summary

ApplyMint is seeking a data science/AI engineer to design and productionize intelligent NLP applications in fintech. You will build RAG pipelines, integrate LLMs, and develop REST APIs with FastAPI/Flask, working as an individual contributor.

The ideal candidate has 3+ years in AI/NLP, strong Python backend skills, and hands-on experience with PyTorch/TensorFlow, LLMs, and prompt engineering. Experience with vector search and MLOps is a plus.

Qualifications

• 3+ years hands-on experience in DS/AI/NLP/GenAI application development.
• Strong Python backend and API development experience.
• Experience with PyTorch/TensorFlow/Keras/Scikit-learn.
• Hands-on NLP use cases: text classification, sentiment, NER, semantic search, embeddings.
• Experience with LLMs such as GPT, LLaMA, Mistral, Phi, Claude, Gemini.
• Experience building or implementing RAG, vector search, semantic search.
• Prompt Engineering and frameworks like LangChain / Azure OpenAI.
• APIs with Python frameworks such as FastAPI/Flask.
• CTC breakup offered: 75% fixed + 25% variable.

Responsibilities

• Design and develop intelligent AI-based applications using NLP and LLM techniques.
• Build and optimize RAG pipelines with structured and unstructured data.
• Integrate LLMs/SLMs for QA, summarization, semantic search, and document understanding.
• Develop and maintain RESTful APIs to serve NLP models and chat interfaces.
• Apply advanced prompting techniques for model efficiency.
• Implement semantic/hybrid search and vector databases (FAISS, Pinecone, Weaviate).
• Perform NLP tasks: NER, text classification, sentiment analysis, embeddings.
• Monitor and fine-tune model performance with real user data.
• Exposure to MLOps tools for monitoring, evaluation, and versioning.
• Build/train/evaluate deep learning models for NLP and traditional ML models.

Skills

Data Scientist

AI Engineer

Generative AI

Education

B.Tech / M.Tech from Tier 1 Colleges

Tools

Python

FastAPI

Flask

PyTorch

TensorFlow

Keras

Scikit-learn

Job description

Roles & Responsibilities

• Design and develop intelligent AI-based applications using advanced NLP and LLM techniques to solve real-world business challenges in financial services.
• Build and optimize Retrieval-Augmented Generation (RAG) pipelines leveraging structured and unstructured financial data.
• Integrate and orchestrate LLMs/SLMs for question-answering, summarization, semantic search, and document understanding.
• Develop and maintain RESTful APIs (sync and async) to serve NLP models and chatbot interfaces using frameworks like FastAPI, Flask, etc.
• Should have knowledge of advanced prompting techniques.
• Implement semantic search, hybrid search, and text retrieval systems using Elasticsearch and vector databases (e.g., FAISS, Pinecone, Weaviate).
• Perform NLP tasks such as entity recognition, text classification, intent detection, embedding generation, and sentiment analysis where required.
• Monitor and fine-tune LLM/SLM performance with real-world user data to improve relevance, latency, and accuracy.
• Exposure to LLMOps tools for monitoring, evaluation, and versioning of AI models in production.
• Build, train, and evaluate deep learning models for NLP tasks including classification, NER, summarization, and embedding generation.
• Develop traditional machine learning models (e.g., regression, decision trees, clustering) for structured data analysis and prediction tasks.
• Interact with cross-functional teams to understand system issues and follow up with respective teams to get them fixed.
• Understand and identify areas of improvement across businesses and participate in solution identification and implementation.
• Should be able to work as an Individual Contributor on new and existing projects.
• Positive and problem-solving attitude, must work as an independent contributor.

Ideal Candidate

• 1. Strong Data Scientist / AI Engineer / Generative AI Engineer profile.
• 2. Mandatory (Experience 1) - Must have 3+ years of hands-on experience in Data Science, Artificial Intelligence, Machine Learning, Deep Learning, NLP, or Generative AI application development.
• 3. Mandatory (Experience 2) - Must have strong hands-on experience in Python programming, backend development, API development, and production-grade application support.
• 4. Mandatory (Experience 3) - Must have experience working with Machine Learning and Deep Learning frameworks such as PyTorch, TensorFlow, Keras, or Scikit-learn.
• 5. Mandatory (Experience 4) - Must have hands-on experience in NLP use cases such as text classification, sentiment analysis, entity recognition (NER), semantic search, embeddings, or document understanding.
• 6. Mandatory (Experience 5) - Must have experience working with Large Language Models (LLMs) such as GPT, LLaMA, Mistral, Phi, Claude, Gemini, or similar models.
• 7. Mandatory (Experience 6) - Must have hands-on experience building or implementing Retrieval Augmented Generation (RAG) solutions, vector search, semantic search, or knowledge-based AI applications.
• 8. Mandatory (Experience 7) - Must have experience with Prompt Engineering and Generative AI frameworks such as LangChain, LangGraph, AI Agents, Azure OpenAI, or similar technologies.
• 9. Mandatory (Experience 8) - Must have experience developing, consuming, or integrating APIs using Python frameworks such as FastAPI, Flask, or similar technologies.
• 10. Mandatory (CTC) - The CTC breakup offered will be 75% fixed + 25% variable, as per company policy.
• 11. Preferred (Experience 1) - Experience with LLMOps/MLOps tools for monitoring, evaluation, experimentation, and versioning of AI models.
• 12. Preferred (Experience 2) - Exposure to Azure OpenAI, Azure Kubernetes Service (AKS), Kubernetes, cloud-native AI deployments, or distributed systems.
• 13. Preferred (Experience 3) - Experience working with PostgreSQL, MongoDB, Redis, Kafka, or large-scale data platforms.
• 14. Preferred (Experience 4) - Familiarity with Docker, Kubernetes, cloud platforms, and scalable deployment architecture.
• 15. Preferred (Company) - Candidates from AI-first startups, product companies, SaaS organizations, fintech, or data-driven technology companies.
• 16. Mandatory ( Age ) - Candidate Should be Below 28 Years.
• 17. Mandatory ( Pedigree) - B.TECH / M.TECH from Tier 1 Colleges (IIT's, NIT's, BITS) are Considered.