Senior AI/Automation Engineer

Location: Boston, MA
Work Model: Hybrid – 4 Days Onsite

Position Overview

We are seeking a Senior AI/Automation Engineer to lead the next phase of AI-enabled Software Development Life Cycle (SDLC) transformation across our Investment Technology organization.

This role will be responsible for defining, building, and scaling AI-driven automation across the entire software delivery lifecycle using Anthropic Cloud Code, Large Language Models (LLMs), agentic AI workflows, and enterprise-grade Generative AI platforms.

The ideal candidate has hands-on experience implementing LLM-powered solutions at enterprise scale, a deep understanding of modern SDLC practices, and a strong focus on automation, governance, security, and measurable business outcomes. This position will play a key role in transforming how software is designed, developed, tested, deployed, and governed.

Key Responsibilities

AI-Enabled SDLC Transformation

• Define and execute a GenAI-powered SDLC strategy across the software development lifecycle.
• Embed AI capabilities into requirements gathering, design, development, testing, deployment, and audit processes.
• Drive automation across:
  • Specification-to-Code
  • Code-to-Test
  • Test-to-Deployment
• Ensure AI-driven workflows align with enterprise SDLC standards and engineering best practices.
• Partner with Architecture, DevSecOps, Risk, and Compliance teams to implement secure, governed, and auditable AI solutions.

Anthropic Cloud Code & Agentic AI

• Serve as the subject matter expert for Anthropic Cloud Code and agentic AI capabilities.
• Define and implement:
  • Prompt engineering standards
  • Agent orchestration patterns
  • Secure model invocation
  • Policy enforcement
• Design and develop AI agent workflows to automate:
  • Requirements elaboration and decomposition
  • Jira story creation
  • Acceptance criteria generation
  • Unit, Integration, and UAT test generation
  • SDLC documentation and compliance artifacts

Enterprise AI Platform Enablement

• Integrate AI automation into enterprise engineering platforms, including CI/CD pipelines, SDLC tools, and cloud environments.
• Develop reusable AI frameworks, components, templates, and governance guardrails.
• Create reference architectures and implementation patterns for engineering teams.
• Drive enterprise adoption of AI through developer enablement and best practices.

Value Realization & Continuous Improvement

• Measure and improve engineering productivity through AI-driven automation.
• Define KPIs and success metrics related to:
  • Development productivity
  • Software quality
  • SDLC cycle time
  • Developer experience
  • Risk reduction
• Provide executive-level reporting on AI adoption, engineering efficiency, and SDLC maturity.

Required Qualifications

• Bachelor''s or Master''s degree in Computer Science, Software Engineering, Information Technology, or a related field.
• 8+ years of experience in Software Engineering, Automation, DevOps, or Platform Engineering.
• 3+ years of hands-on experience with Generative AI and Large Language Models (LLMs).
• Strong experience implementing enterprise AI automation solutions.
• Deep understanding of Software Development Life Cycle (SDLC) methodologies.
• Experience with AI agents, prompt engineering, and workflow orchestration.
• Strong programming skills in Python, Java, or similar languages.
• Experience with CI/CD pipelines and DevSecOps practices.
• Hands-on experience with Jira and enterprise software delivery tools.
• Excellent communication and stakeholder management skills.

Preferred Qualifications

• Experience with Anthropic Claude or Anthropic Cloud Code.
• Experience with OpenAI, Azure OpenAI, or similar enterprise LLM platforms.
• Experience integrating AI into requirements management, software development, testing automation, and SDLC governance.
• Knowledge of enterprise AI governance, model risk management, security, and auditability.
• Experience working within financial services or other highly regulated industries.
• Experience with AI frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or similar agentic AI platforms.
• Experience with cloud platforms such as AWS, Azure, or Google Cloud.
• Strong understanding of modern DevOps and platform engineering practices.

Required Technical Skills

• Generative AI
• Large Language Models (LLMs)
• Anthropic Claude / Anthropic Cloud Code
• Prompt Engineering
• Agentic AI
• AI Workflow Automation
• Python or Java
• SDLC
• CI/CD
• DevOps / DevSecOps
• Jira
• Git
• Cloud Platforms (AWS, Azure, Google Cloud Platform)
• Test Automation
• Enterprise AI Governance

Success Metrics

Success in this role will be measured by:

• Reduced SDLC cycle time through AI-driven automation.
• Increased engineering productivity and software quality.
• Successful adoption of AI-generated requirements, code, and test artifacts within governed workflows.
• Standardization of Anthropic Cloud Code capabilities as an enterprise platform.
• Demonstrable improvements in SDLC maturity, automation, compliance, and operational efficiency across the organization.