Job Summary: We are seeking a highly skilled professional to design and build Agentic AI-driven solutions to automate SAS to PySpark code conversion and validation. The role will leverage AWS Bedrock (Claude), Retrieval-Augmented Generation (RAG), and multi-agent orchestration to enable intelligent code transformation and automated testing frameworks, particularly in actuarial/data domains. This position is part of a strategic data modernization initiative with strong focus on automation, scalability, and accuracy.

Key Responsibilities: Design and develop agent-based AI systems for: Automated SAS $B"*(J PySpark code conversion Intelligent test case generation and validation Build and orchestrate multi-agent workflows leveraging LLMs (Claude) for: Code parsing, transformation, optimization, and validation Implement RAG pipelines to retrieve reusable patterns, business rules, and legacy logic during code conversion Develop and execute automated testing frameworks to compare SAS vs PySpark outputs (data reconciliation, validation checks) Integrate solutions with AWS services (Bedrock, S3, Glue, Lambda, API Gateway, etc.) Collaborate with actuarial/business teams to interpret domain-specific logic and ensure functional equivalence Build reusable accelerators for large-scale legacy modernization programs Ensure robustness through logging, monitoring, and evaluation of agent outputs Contribute to best practices for AI-driven software engineering and autonomous pipelines

Required Skills: Strong experience in Python & PySpark development Hands-on experience in AWS (Glue, S3, Lambda, Bedrock, CloudWatch) Experience with LLMs (Claude / OpenAI / similar) and prompt engineering Expertise in RAG architectures and vector-based retrieval systems Experience in SAS programming / SAS data pipelines (critical for conversion logic) Knowledge of ETL/data engineering and large-scale data migration Familiarity with agentic AI frameworks / multi-agent orchestration concepts [Agentic AI...cture Dive | SharePoint] Strong experience in data validation and testing frameworks Exposure to LangChain / LangGraph / agent frameworks

Nice to Have: Experience in actuarial / insurance domain (pricing, risk models, etc.) Familiarity with MLOps / CI-CD for AI pipelines

Experience: 8–12+ years (Senior Consultant)