Senior Engineer, AI Engineering (R5459)

What you'll do:

AI Solution Delivery & Productivity Enablement
Build AI-assisted tools, workflow automations, agents, prompts, and integrations that reduce manual effort and improve individual and team productivity.
Partner with business stakeholders to understand high-friction workflows, translate them into technical requirements, and deliver fit-for-purpose AI solutions.
Implement AI-augmented collaboration patterns such as meeting intelligence, document generation, contextual knowledge retrieval, task automation, and internal assistant workflows.
Develop and maintain internal enablement assets including prompt templates, agent examples, skill templates, playbooks, and usage guidance.
Collect user feedback and operational telemetry to improve adoption, usability, reliability, and measured impact.
Reusable Components & Integrations
Build and maintain reusable AI components including connectors, integration adapters, prompt modules, data pipelines, skill templates, and service wrappers.
Contribute to shared component libraries using established quality, documentation, versioning, testing, and deprecation practices.
Integrate AI capabilities with enterprise systems, collaboration tools, knowledge repositories, data platforms, and workflow automation platforms.
Create developer-facing documentation, examples, and onboarding material that help other teams adopt shared AI components safely and efficiently.
Identify repeatable patterns from project work and convert them into reusable assets for broader enterprise use.
Responsible AI Controls & Operations
Implement engineering controls for data handling, access management, prompt safety, output validation, audit logging, and secure integration patterns.
Follow enterprise AI architecture and governance standards while escalating gaps, risks, or implementation challenges to technical leads.
Build or maintain dashboards for AI usage, adoption, policy adherence, cost visibility, error patterns, and operational health.
Support model, prompt, and agent lifecycle activities such as evaluation, version tracking, testing, rollout, monitoring, and rollback.
Participate in security, privacy, and governance reviews by providing implementation details, evidence, and remediation support.
Cost, ROI & Cross-Functional Execution
Instrument AI solutions to capture usage, performance, cost, quality, and productivity metrics.
Support cost optimization work through usage analysis, model efficiency improvements, license rationalization inputs, and service tuning.
Help connect AI solution usage to measurable outcomes such as time savings, error reduction, throughput improvement, and capacity creation.
Collaborate with Engineering, IT, Security, Legal, Data, Finance, and business unit teams to deliver reliable AI capabilities in a matrixed environment.
Contribute to AI communities of practice by sharing lessons learned, reusable patterns, demos, and implementation guidance.

Required qualifications:

Progressive experience building enterprise software, automation, data, AI, or digital workplace solutions.
Hands-on experience integrating large language models, generative AI tools, APIs, RAG systems, agents, prompt workflows, or AI-assisted automation into production or enterprise environments.
Strong software engineering fundamentals including API design, testing, observability, documentation, secure coding practices, and maintainable implementation patterns.
Experience building integrations with enterprise systems, collaboration platforms, knowledge repositories, data platforms, or workflow automation tools.
Working knowledge of AI governance concepts such as access controls, data classification, audit logging, prompt safety, output validation, and model/prompt versioning.
Ability to convert ambiguous business workflows into practical technical solutions in partnership with stakeholders.
Experience instrumenting systems with telemetry, logging, dashboards, usage metrics, or cost/performance monitoring.
Clear communication skills and a collaborative style suitable for working across business, engineering, security, legal, and data teams.

Preferred qualifications:

Experience in regulated, security-sensitive, defense-adjacent, or data-governed environments.
Familiarity with enterprise AI tooling ecosystems including copilot platforms, workflow automation suites, RAG platforms, vector databases, and enterprise search.
Experience with MLOps, model evaluation, AI observability, prompt/agent testing, or production monitoring.
Hands-on experience with data platforms such as Databricks, Snowflake, lakehouse architectures, or equivalent data infrastructure.
Experience developing usage dashboards, cost reporting, showback inputs, or ROI measurement for shared technology services.
Experience contributing to reusable component libraries, internal developer platforms, templates, or enablement playbooks.
Degree in Computer Science, Engineering, Data Science, or a related technical field, or equivalent practical experience.