# Senior Engineer, AI Engineering

**Company:** [Shield AI](https://hotfix.jobs/companies/shield-ai)
**Location:** San Diego, CA, San Francisco, CA
**Role:** ML Engineering
**Salary:** $160k – $290k/yr
**Experience:** 7+ years
**Skills:** Generative AI, LLMs, RAG, AI Agents, Prompt Engineering, API Design, MLOps, Databricks, Snowflake, Vector Databases, Python, Observability, ai governance
**Posted:** 2026-07-24

> Senior hands-on AI Engineer building production AI agents, prompts, integrations, automations, and reusable components to accelerate enterprise AI adoption. Requires strong software engineering skills, experience integrating LLMs/RAG/agents into enterprise systems, and knowledge of responsible AI controls, telemetry, and governance.

## Job Description

## 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.

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