Python AI Engineer - LLM & Agentic AI (Finance Data Platform)

Mployee.me; Bengaluru; Híbrido; INR 4.000.000 - 6.000.000; Jornada completa

Descripción de la vacante

Wissen Technology is hiring a Python AI Engineer to build and maintain AI-powered finance applications for global financial services clients. The role focuses on product development using Python, LLMs, Agentic AI frameworks, and RAG architectures to help users discover, understand, and act on finance and operational data at scale.

The position is based in Bangalore with hybrid mode of work, and requires 4-6 years of experience in core Python, LLM engineering, and end-to-end AI product features.

Formación

• 4-6 years of experience in Python AI engineering.
• Experience building AI-powered finance solutions with production-grade code.
• Experience with LLMs, embeddings, and vector databases.

Responsabilidades

• Build Python services for processing finance data (structured and unstructured).
• Design end-to-end RAG pipelines with ingestion, chunking, embeddings, and vector search.
• Develop AI agents/workflows using LangChain and LangGraph.
• Integrate LLMs with APIs, databases, and enterprise platforms.
• Evaluate LLM outputs for quality and reduce hallucinations.
• Optimize latency, reliability, and cost of production AI systems.
• Implement security and access controls in regulated environments.
• Collaborate with product owners and data engineers to deliver enterprise AI solutions.

Conocimientos

Core Python

LLM Engineering

LangChain

LangGraph

RAG development

API development

Pandas

SQL

Git & testing

Educación

BE/BTech/ME/MTech in CS/AI/DS

Herramientas

Databricks

Mosaic AI

Vector search

LangSmith / AI observability

AWS/Azure/GCP

CI/CD

Descripción del empleo

Job Summary

Wissen Technology is hiring a Python AI Engineer to build and maintain AI-powered finance applications for global financial services clients. The role focuses on product development using Python, LLMs, Agentic AI frameworks, and RAG architectures to help users discover, understand, and act on finance and operational data at scale.

Experience, Location, Mode of Work

• Experience : 4-6 years
• Location : Bangalore
• Mode of Work : Hybrid

Mandatory Skills (Must Have)

• Core Python (4+ years with OOP, data structures, error handling, and production-grade development)
  
• LLM Engineering using OpenAI, Azure OpenAI, Anthropic, or open-source models
  
• LangChain and LangGraph for Agentic AI workflows and tool orchestration
  
• RAG development including embeddings, vector databases, retrieval tuning, and grounding
  
• FastAPI or Flask REST API development and integrations
  
• Pandas and structured/unstructured data processing for AI applications
  
• SQL and relational database querying in enterprise environments
  
• Git, unit testing, code reviews, and software engineering best practices
  

Optional Skills (Good to Have)

• Databricks, Mosaic AI, Vector Search, Unity Catalog, or MLflow
  
• Spark/PySpark data processing
  
• Financial services, finance, accounting, GL, reconciliation, or reporting domain knowledge
  
• Oracle Cloud or Oracle Fusion data integrations
  
• LangSmith, RAGAS, DeepEval, or AI observability tools
  
• AWS, Azure, GCP, Docker, and CI/CD pipelines
  

Professional Attributes & Qualifications

• Education: BE/BTech/ME/MTech in Computer Science, Artificial Intelligence, Data Science, Engineering, or related field
  
• Leadership: Experience mentoring engineers and guiding technical implementation decisions
  
• Ownership: Ability to independently drive end-to-end AI product features and solutions
  
• Communication: Strong written and verbal communication skills for technical and business stakeholders
  
• Problem Solving: Ability to explain technical trade-offs and recommend scalable AI architectures
  

Key Responsibilities

• Build Python services for processing and transforming structured and unstructured finance data
  
• Design and develop end-to-end RAG pipelines with document ingestion, chunking, embeddings, and vector search
  
• Build AI agents and workflows using LangChain and LangGraph
  
• Integrate LLMs with APIs, databases, and enterprise finance platforms
  
• Evaluate LLM responses for quality, relevance, accuracy, and hallucination reduction
  
• Optimize latency, reliability, scalability, and cost of production AI systems
  
• Implement security, privacy, and access-control practices in regulated environments
  
• Collaborate with product owners, data engineers, and stakeholders to deliver enterprise AI solutions