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

About the job:Key Responsibilities:

1. Design and develop AI/ML applications using NLP and LLM technologies for real-world financial services use cases.

2. Build and optimize RAG pipelines using structured and unstructured financial data.

3. Develop REST APIs (sync/async) for NLP models and chatbot applications using FastAPI, Flask, etc.

4. Apply advanced prompting techniques to improve LLM performance and output quality.

5. Build semantic and hybrid search systems using Elasticsearch and vector databases such as FAISS, Pinecone, or Weaviate.

6. Work on NLP tasks including NER, text classification, intent detection, embeddings, and sentiment analysis.

7. Monitor, evaluate, and optimize LLM/SLM performance using real-world data to improve accuracy, relevance, and latency.

8. Work with LLMOps tools for model monitoring, evaluation, and versioning in production.

9. Build, train, and evaluate deep learning models for NLP use cases such as classification, NER, summarization, and embeddings.

10. Develop traditional ML models for structured data analysis and predictive use cases.

11. Collaborate with cross-functional teams to identify issues, drive solutions, and implement improvements.

12. Identify opportunities for process and business improvements and contribute to solution design and implementation.

13. Work as an Individual Contributor (IC) across new and existing projects.

14. Demonstrate a positive, proactive, and problem-solving attitude with the ability to work independently.

15. Integrate and orchestrate LLMs/SLMs for Q&A, summarization, semantic search, and document understanding.

Note: CTC: 75% Fixed + 25% Variable.Who can apply:Only those candidates can apply who:have minimum 1 years of experienceSalary:₹ 19,00,000 - 31,00,000 /yearExperience:1 year(s)Deadline:[hidden]:59:59Other perks:5 days a weekSkills required:Python, SQL, Data Science, Natural Language Processing (NLP) and Generative AI ToolsOther Requirements:1. Strong Data Scientist/AI Engineer/Generative AI Engineer profile.2. 1+ years hands-on experience in Data Science, AI/ML, Deep Learning, NLP, or GenAI.3. Strong Python, backend, API development, and production support experience.4. Hands-on with PyTorch, TensorFlow, Keras, or Scikit-learn.5. Experience in NLP: classification, sentiment analysis, NER, semantic search, embeddings, or document understanding.6. Hands-on experience with LLMs such as GPT, LLaMA, Mistral, Phi, Claude, Gemini, or similar.7. Experience building RAG, vector search, semantic search, or knowledge-based AI solutions.8. Strong Prompt Engineering and experience with LangChain, LangGraph, AI Agents, Azure OpenAI, or similar.9. Experience with FastAPI, Flask, or similar Python API frameworks.10. Age: Below 28 years.11. Education: BTech/MTech from Tier-1 colleges – IIT, NIT, BITS, IIIT, DTU, NSUT.12. LLMOps/MLOps, Azure OpenAI, AKS, Kubernetes, Docker, or cloud-native AI.13. PostgreSQL, MongoDB, Redis, Kafka, or large-scale data platforms.14. Candidates from AI-first startups, product/SaaS, fintech, or data-driven technology companies.About Company:TalentXO is a recruitment-tech startup that combines the power of a network of 200+ (growing fast) talent partners and domain experts, and a super-intelligent platform to deliver on your most challenging hiring goals. We are building a super recruitment platform that understands the complexities of hiring top talent and can solve the problems of attracting candidates, checking company fitment, skill assessment, and more through our network of talent partners and domain experts. We are trusted by top companies-10X Talent for 10X Growth-Bajaj Finserv, Razorpay, Myntra, Directi, Byju's App, InMobi, Deloitte, Wipro, Nykaa, Pharmeasy, Delhivery, Meesho, and many more. TalentXO was established in 2016 and has grown organically with the blessing of our many happy clients. We are a bunch of talented and motivated folks and are excited to continue our innovation-driven growth story.

Responsibilities

Key Responsibilities:

1. Design and develop AI/ML applications using NLP and LLM technologies for real-world financial services use cases.

2. Build and optimize RAG pipelines using structured and unstructured financial data.

3. Develop REST APIs (sync/async) for NLP models and chatbot applications using FastAPI, Flask, etc.

4. Apply advanced prompting techniques to improve LLM performance and output quality.

5. Build semantic and hybrid search systems using Elasticsearch and vector databases such as FAISS, Pinecone, or Weaviate.

6. Work on NLP tasks including NER, text classification, intent detection, embeddings, and sentiment analysis.

7. Monitor, evaluate, and optimize LLM/SLM performance using real-world data to improve accuracy, relevance, and latency.

8. Work with LLMOps tools for model monitoring, evaluation, and versioning in production.

9. Build, train, and evaluate deep learning models for NLP use cases such as classification, NER, summarization, and embeddings.

10. Develop traditional ML models for structured data analysis and predictive use cases.

11. Collaborate with cross-functional teams to identify issues, drive solutions, and implement improvements.

12. Identify opportunities for process and business improvements and contribute to solution design and implementation.

13. Work as an Individual Contributor (IC) across new and existing projects.

14. Demonstrate a positive, proactive, and problem-solving attitude with the ability to work independently.

15. Integrate and orchestrate LLMs/SLMs for Q&A, summarization, semantic search, and document understanding.

Note: CTC: 75% Fixed + 25% Variable.