Requirement - Senior AI/Automation Engineer
Location- Boston, MA and Minneapolis, MN(4 days a week on-site)
Mode of Interview : 3 rounds, all virtual
Contract W2
We are seeking a Senior AI Automation Engineer to lead the next phase of AI-enabled SDLC maturity across Investment Technology. This role is responsible for defining, building, and scaling AI-driven automation across the software delivery lifecycle—leveraging Anthropic Cloud Code–based capabilities, agentic workflows, and enterprise-grade GenAI platforms.
The ideal candidate brings hands-on experience operationalizing LLMs at scale, deep understanding of modern SDLC practices, and a strong bias toward automation, governance, and measurable business outcomes. This role will directly influence how software is designed, built, tested, and deployed across Ameriprise.
AI-Enabled SDLC Transformation
· Define and execute a GenAI-augmented SDLC strategy, embedding AI across requirements, design, development, testing, deployment, and auditability.
· Drive spec-to-code, code-to-test, and test-to-deployment automation using LLM-powered workflows aligned to enterprise SDLC standards 1.
· Partner with Architecture, DevSecOps, Risk, and Compliance teams to ensure secure, governed, and auditable AI adoption.
Anthropic Cloud Code & Agentic Automation
· Serve as the subject matter expert for Anthropic Cloud Code capabilities, including:
o Prompt engineering standards
o Agent-based orchestration patterns
o Secure model invocation and policy enforcement
· Design and deploy agentic AI workflows to automate:
o Requirements for elaboration and decomposition
o Jira story and acceptance criteria generation
o Unit, integration, and UAT test generation
o SDLC artifact and control evidence creation
Enterprise Platform Enablement
· Integrate AI automation into existing enterprise platforms (e.g., CI/CD pipelines, SDLC tooling, cloud platforms).
· Establish reusable AI components, frameworks, and guardrails for product and engineering teams.
· Enable adoption through reference architectures, implementation patterns, and developer enablement.
Value Realization & Measurement
· Identify and quantify productivity, quality, and cycle-time improvements driven by AI.
· Define KPIs and success metrics tied to SDLC efficiency, developer experience, and risk reduction.
· Support executive visibility into AI-driven outcomes and maturity progress.
Preferred Qualifications
· Experience integrating GenAI into requirements management, testing automation, and SDLC controls.
· Familiarity with enterprise GenAI governance models, including model risk management and auditability.
· Experience working in financial services or highly regulated industries.
· Thought leadership in AI platforms, automation, or engineering productivity initiatives.
What Success Looks Like
· AI-driven automation measurably reduces SDLC cycle time and manual effort.
· Development teams consistently leverage AI-generated requirements, code, and tests within governed workflows.
· Anthropic Cloud Code capabilities are adopted as a standard, reusable enterprise platform capability.
· Ameriprise achieves a demonstrable increase in SDLC maturity, quality, and