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

Wissen Technology is hiring for Python AI Engineer – LLM & Agentic AI (Finance Data Platform)

ABOUT WISSEN TECHNOLOGY

At Wissen Technology, we deliver niche, custom-built products that solve complex business challenges across industries worldwide. Founded in 2015, our core philosophy is built around a strong product engineering mindset—ensuring every solution is architected and delivered right the first time. Today, Wissen Technology has a global footprint with 2000+ employees across offices in the US, UK, UAE, India, and Australia. Our commitment to excellence translates into delivering 2X impact compared to traditional service providers. How do we achieve this? Through a combination of deep domain knowledge, cutting-edge technology expertise, and a relentless focus on quality. We don't just meet expectations—we exceed them by ensuring faster time-to-market, reduced rework, and greater alignment with client objectives. We have a proven track record of building mission-critical systems across industries, including financial services, healthcare, retail, manufacturing, and more. Wissen stands apart through its unique delivery models. Our outcome-based projects ensure predictable costs and timelines, while our agile pods provide clients the flexibility to adapt to their evolving business needs. Wissen leverages its thought leadership and technology prowess to drive superior business outcomes. Our success is powered by top-tier talent. Our mission is clear: to be the partner of choice for building world-class custom products that deliver exceptional impact—the first time, every time.

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
  

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