Senior Engineer, AI Engineering

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.