Sr AI Automation Engineer - SDLC & GenAI Platforms

Ameriprise Financial Services, LLC•Boston, MA

23h•$128,000 - $168,000

About The Position

Ameriprise Financial is 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.

Requirements

Hands-on experience operationalizing LLMs at scale.
Deep understanding of modern SDLC practices.
Strong bias toward automation, governance, and measurable business outcomes.
Subject matter expert for Anthropic Cloud Code capabilities.
Experience integrating AI automation into existing enterprise platforms (e.g., CI/CD pipelines, SDLC tooling, cloud platforms).

Nice To Haves

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.

Responsibilities

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.
Partner with Architecture, DevSecOps, Risk, and Compliance teams to ensure secure, governed, and auditable AI adoption.
Serve as the subject matter expert for Anthropic Cloud Code capabilities, including: Prompt engineering standards, Agent-based orchestration patterns, Secure model invocation and policy enforcement.
Design and deploy agentic AI workflows to automate: Requirements for elaboration and decomposition, Jira story and acceptance criteria generation, Unit, integration, and UAT test generation, SDLC artifact and control evidence creation.
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.
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.