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# Principal Software Developer (Generative AI)

- **Company:** MCKINLEY GROUP INC
- **Location:** Bloomington, MN, United States
- **Salary:** $228,800 - $239,200
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Amazon Web Services, Automation of Tests, Code Review, Distributed Systems, Github, Information Retrieval, Microsoft SQL Server, Software Engineering, SQL Databases, Datadog, Sql Optimization, Retrieval-Augmented Generation, Spring-boot, Generative AI, Indexer, Pgvector, Amazon Bedrock, Production Code, Claude, Restful APIs, Terraform, Dynatrace, OpenSearch, Amazon Redshift, Microservices
- **Published:** November 14, 2026
- **Apply:** https://www.careerboard.com/us/en/find-jobs-in-United-States/-394E24A93E3D2EBE02/

## About the Role

* 12+ years professional software development experience (or equivalent combination of education/training/experience).
 * Advanced Java with Spring/Spring Boot, REST APIs, microservices, distributed systems, and automated testing.
 * Hands-on delivery of generative AI applications in a production or production-oriented environment.
 * Practical experience designing RAG systems (embeddings, retrieval, context assembly, source attribution).
 * Strong AWS experience and familiarity with Amazon Bedrock (or comparable managed model platform).
 * Advanced SQL and experience with SQL Server and/or Amazon Redshift.
 * Strong security mindset: authN/authZ, tenant isolation, least privilege, governance, responsible AI controls

Nice-to-Have

 * Bedrock guardrails/agents; Claude via Bedrock; OpenSearch or pgvector.
 * Datadog, GitHub Actions, Terraform, distributed tracing.
 * Text-to-SQL/semantic modeling/natural-language analytics.

## Description

* Design, build, test, and operate generative AI features for a customer-facing platform.
 * Develop conversational and agentic experiences that retrieve authorized information and produce grounded, explainable responses.
 * Architect and implement RAG pipelines (ingestion, chunking, embeddings, indexing, hybrid retrieval, reranking, citations, freshness, permission-aware access).
 * Build agent/tool interfaces with strong authorization, validation, retries/timeouts, and safe multi-step execution patterns.
 * Integrate managed foundation models (including Amazon Bedrock) into Java/Spring services.
 * Create automated evaluation suites and datasets (groundedness, retrieval relevance, citation accuracy, safety, regressions).
 * Instrument end-to-end observability (latency, token usage, outcomes, failures) and support production readiness.
 * Lead architecture/code reviews and mentor engineers while staying hands-on in production code.