Python AI Engineer - LLM & Agentic AI (Finance Data Platform)
Wissen Infotech · Bengaluru · onsite · Posted 1d ago
Undisclosed4–6 yrsfulltimeSourced from
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About this role
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
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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
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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
- Collaborate with product owners, data engineers, and stakeholders to deliver enterprise AI solutions
- Implement security, privacy, and access-control practices in regulated environments
- Optimize latency, reliability, scalability, and cost of production AI systems
- Evaluate LLM responses for quality, relevance, accuracy, and hallucination reduction
- Integrate LLMs with APIs, databases, and enterprise finance platforms
- Build AI agents and workflows using LangChain and LangGraph
- Design and develop end-to-end RAG pipelines with document ingestion, chunking, embeddings, and vector search
- Build Python services for processing and transforming structured and unstructured finance data
-
- Problem Solving: Ability to explain technical trade-offs and recommend scalable AI architectures
- Communication: Strong written and verbal communication skills for technical and business stakeholders
- Ownership: Ability to independently drive end-to-end AI product features and solutions
- Leadership: Experience mentoring engineers and guiding technical implementation decisions
- Education: BE/BTech/ME/MTech in Computer Science, Artificial Intelligence, Data Science, Engineering, or related field
-
- AWS, Azure, GCP, Docker, and CI/CD pipelines
- LangSmith, RAGAS, DeepEval, or AI observability tools
- Oracle Cloud or Oracle Fusion data integrations
- Financial services, finance, accounting, GL, reconciliation, or reporting domain knowledge
- Spark/PySpark data processing
- Databricks, Mosaic AI, Vector Search, Unity Catalog, or MLflow
-
- Git, unit testing, code reviews, and software engineering best practices
- SQL and relational database querying in enterprise environments
- Pandas and structured/unstructured data processing for AI applications
- FastAPI or Flask REST API development and integrations
- RAG development including embeddings, vector databases, retrieval tuning, and grounding
- LangChain and LangGraph for Agentic AI workflows and tool orchestration
- LLM Engineering using OpenAI, Azure OpenAI, Anthropic, or open-source models