Why this grade
This listing scored 52/100, which is a D. It lost the most ground on freshness.
- Pay transparency 25 / 25 A published salary range, worth more than any other single factor because it is what a candidate cannot find out without applying.
- Description depth 20 / 20 How much the posting actually says about the work, measured in characters of real text.
- Corroboration 10 / 10 Whether more than one source carries this listing.
- Remote clarity 8 / 15 Whether "remote" means anywhere, or is quietly restricted to one country.
- Freshness 4 / 15 How recently it was posted. Older postings are likelier to be filled or abandoned.
- Role specificity 0 / 10 Whether the listing is tagged well enough to tell what the role actually is.
-15 Ghost-job penalty — Deducted for signals that this posting may not be a real, currently-open role — staleness, repeated relisting, or talent-pool language.
Every figure above is arithmetic over the posting itself — its salary field, its text, its age, its tags and how many sources carry it. How the grades work →
Job Description:
The Senior Engineer, AI Engineering is a hands-on individual contributor responsible for building and operating AI-enabled solutions, reusable components, integrations, automations, and measurement capabilities that accelerate enterprise AI adoption. Reporting into the AI Engineering organization, this role works closely with the Staff Engineer, AI Platform & Architecture and the Director, AI Engineering to convert high-friction workflows into secure, reliable, measurable AI capabilities. The Senior Engineer delivers production-quality agents, prompts, connectors, dashboards, and workflow automations while following established architecture, governance, and cost-control standards. Success is defined by shipped capabilities that improve employee productivity, reusable components that reduce duplicate work, reliable telemetry that demonstrates impact, and strong collaboration with business and technology partners.
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.
Originally posted on Himalayas
Apply for this role Opens jobs.lever.co — the employer's own page, as linked by the source that listed this role
Where this listing came from
- 25 Jul 2026 VC talent network employer ATS first sighting
- 27 Jul 2026 Himalayas also listed, 2 days later
Seen on 2 boards over 3 days.