Data Scientist / AI Engineer (Python, ML, RAG)
Listing checked August 17, 2026 · pay as published by Talentxo
Overview
The candidate will own the end-to-end development of AI powered applications at scale. They will work on Retrieval-Augmented Generation pipelines and semantic search over structured and unstructured data. The role involves building and deploying RESTful APIs for NLP models, integrating LLMs and SLMs, and monitoring model performance in production. The team uses Python based backends, FastAPI or Flask, and vector databases such as FAISS or Pinecone to enable advanced NLP capabilities. What makes this role different is the emphasis on production grade MLOps, high volume APIs, and cross-functional collaboration within financial services contexts.
What You'll Do8
- 1Design and develop intelligent AI based applications using advanced NLP and LLM techniques to solve real world business challenges in financial services
- 2Build and optimize Retrieval-Augmented Generation (RAG) pipelines leveraging structured and unstructured financial data
- 3Integrate and orchestrate LLMs/SLMs for question answering, summarization, semantic search, and document understanding
- 4Develop and maintain RESTful APIs (sync and async) to serve NLP models and chatbot interfaces using frameworks like FastAPI, Flask
- 5Monitor and fine tune LLM/SLM performance with real world user data to improve relevance, latency, and accuracy
- 6Exposure to LLMOps tools for monitoring, evaluation, and versioning of AI models in production
- 7Build, train, and evaluate deep learning models for NLP tasks including classification, NER, summarization, and embedding generation
- 8Develop traditional machine learning models for structured data analysis and prediction tasks
Requirements10
- 13+ years of hands on experience in AI Engineering, Data Science, Machine Learning, Deep Learning, NLP, or Generative AI application development
- 2Strong Python programming experience with backend development, REST API development, and production grade application support
- 3Hands on experience with Deep Learning frameworks such as PyTorch, TensorFlow, or Keras for NLP and classification models
- 4Experience working with Large Language Models such as GPT, LLaMA, Mistral, Phi, Claude, or similar models
- 5Hands on experience in building RAG applications, semantic search solutions, and vector-based retrieval systems
- 6Practical experience with Prompt Engineering, LLM fine tuning, embeddings, and AI powered business applications
- 7Hands on experience with LangChain, LangGraph, AI Agents or similar GenAI orchestration frameworks
- 8Experience developing and managing high-volume APIs using FastAPI, Flask, or similar Python frameworks
- 9Exposure to vector databases and semantic search technologies such as Pinecone, FAISS, Weaviate, Elasticsearch
- 10Exposure to LLMOps/MLOps practices including model monitoring, evaluation, versioning, and production support
Who Should Apply
Ideal candidate is a Python focused AI engineer with 3+ years of hands on experience in AI and NLP, possesses production grade API development skills, and has worked with LLMs and RAG systems. They should have strong problem solving, RCA capabilities, and ability to troubleshoot complex issues independently. Familiarity with LangChain or similar GenAI orchestration tools and vector databases is preferred.
Salary Insight
Open to Discussion
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Prepare a concise portfolio section highlighting a RAG project with vector store integration and a production API, including metrics on latency and accuracy.
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