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About This Role
### Software Development Engineer \-AI/Agentic Systems
- JR\-161117
- Hybrid
- Redwood City
- Technology
- Full time
Who are we?
Equinix is the world’s digital infrastructure company®, shortening the path to connectivity to enable the innovations that enrich our work, life and planet.
A place where tech thinkers and future builders turn bold ideas into breakthrough experiences, we welcome your unique perspective.
Help us challenge assumptions, uncover bias, and remove barriers—because progress starts with fresh ideas. You’ll find belonging, purpose, and a team that welcomes you—because when you feel valued, you’re empowered to do your best work.
Job Summary
The Senior Requirements Engineer for AI Agents \& Agentic Workflows is a critical role within the Developer Experience Program (EDP), responsible for defining and shaping the requirements for Equinix's most forward\-looking developer productivity investments. You will own the requirements for an agentic execution framework, Claude Code platform integration, an AI Agent Registry, and a low\-code agent build platform. This role sits at the intersection of developer experience, AI/ML platform engineering, and product strategy. You will translate ambiguous, high\-impact goals — like reducing PR cycle time by 50% and achieving a 50% developer productivity gain — into precise, actionable requirements that engineering teams can build against. You will work and partner closely with platform engineering, compliance, governance, and AI platform teams
Responsibilities
- Design and build the Agentic Execution Framework: event bus integration, agent scaffolding, human\-in\-the\-loop approval queues, retry and error handling, and end\-to\-end execution traceability
- Implement Claude Code platform\-native integrations: CI failure triage agent, PR assist agent, unit test generation agent, and the "what should I work on?" developer portal surface
- Build and maintain the AI Agent Registry: catalog service, agent metadata schema, discovery APIs, trust boundary enforcement, versioning, and lifecycle management
- Develop the Agent Build Platform: a low\-code, UI graph\-based workflow builder that enables developers without deep agentic knowledge to compose and deploy production\-ready agentic workflows
- Implement agent integration connectors into delivery pipelines, incident management systems, and policy enforcement layers to automate manual handoffs and reduce developer toil
- Build scoped permission models, execution isolation environments, audit logging, and human\-in\-the\-loop guardrails to ensure agents operate safely within enterprise governance boundaries
- Instrument all agentic systems with observability hooks — logs, metrics, traces — and integrate with the unified observability platform
- Write clean, well\-tested, production\-grade code with high coverage; own your services from development through deployment and on\-call
- Participate in architecture design reviews, contribute to engineering standards, and help define patterns that other teams can follow when building agentic systems
- Continuously measure impact against developer productivity metrics — PR cycle time, time to first commit, deployment frequency — and iterate based on data
Technical Requirements
- Strong proficiency in one or more backend languages — Go, Python, Java, or TypeScript — with demonstrable experience building event\-driven or distributed systems in production
- Hands\-on experience building with LLM APIs and agentic frameworks: LangChain, LangGraph, AutoGen, CrewAI, Amazon Bedrock Agents, or equivalents
- Experience integrating with GitHub APIs, GitHub Actions, and CI/CD pipeline systems — building bots, automation workflows, or developer tooling on top of these platforms
- Practical knowledge of event streaming and messaging systems: Kafka, EventBridge, RabbitMQ, or equivalents — able to design reliable, observable event\-driven pipelines
- Experience building and consuming REST and GraphQL APIs; comfortable designing service contracts and integration schemas
- Familiarity with workflow orchestration platforms such as Temporal, n8n.io, Prefect, or similar
- Understanding of developer portal frameworks, particularly Backstage — able to build and publish plugins that surface agentic capabilities in the developer experience layer
- Solid grasp of security patterns relevant to agent systems: non\-human identity, scoped permissions, secrets management, least\-privilege execution, and audit trails
- Experience with containerized workloads and Kubernetes — able to deploy, operate, and scale agent services in a cloud\-native environment
- Proficiency with observability tooling: structured logging, metrics instrumentation (Prometheus/Grafana), and distributed tracing (OpenTelemetry)
Qualifications
- 7\+ years of software engineering experience, with at least 2 years focused on platform engineering, developer tooling, or AI/ML systems in a production environment
- Demonstrated experience shipping agentic or LLM\-powered systems at scale — not just prototypes, but production services with reliability, governance, and observability requirements
- Strong track record of building internal developer platforms, developer portals, or self\-service tooling used by large engineering organizations
- Experience working in large, matrixed engineering environments — comfortable navigating dependencies across security, platform, compliance, and application engineering teams
- Ability to operate with autonomy on ambiguous, greenfield engineering challenges where the patterns are still being defined
- Strong written and verbal communication skills — able to document architecture decisions, write clear technical specs, and engage productively with product and business stakeholders
- Bachelor's degree in Computer Science, Engineering, or a related field; equivalent practical experience accepted
The targeted pay range for this position in the following location is / locations are:
United States \- Redwood City Office GHQ : 186,000 \- 280,000 USD / Annual
Our pay ranges reflect the minimum and maximum target for new hire pay for the full\-time position determined by role, level, and location.The pay range shown is based on our compensation structure in place at the time of posting and may be updated periodically based on business needs. Individual pay is based on additional factors including job\-related skills, experience, and relevant education and/or training.
The targeted pay range listed reflects the base pay only and does not include bonus, equity, or benefits. Employees are eligible for bonus, and equity may be offered depending on the position.
Equinix Benefits
As an employee, you become important to Equinix’s success. We ensure all your benefits are in line with our core values: competitive, inclusive, sustainable, connected and efficient. We keep them competitive within the current marketplace to ensure we’re providing you with the best package possible. So, wherever you are in your career and life, you’ll be able to enhance your experience and bring your whole self to work.
Employee Assistance Program: An Employee Assistance program is available to all employees.
US Benefits: \- Insurance: You may enroll in health, life, disability and voluntary plans that are designed for you and your eligible family members. \- Retirement: You and Equinix may contribute to a retirement plan to help you plan for your financial future. \- Paid Time Off (PTO) and Paid Holidays: You will receive an accrued amount of PTO each pay period along with various paid holidays for you to rest and recharge. Eligibility requirements apply to some benefits. Benefits are subject to change and may be subject to specific plan or program terms.
Equinix is committed to ensuring that our employment process is open to all individuals, including those with a disability. If you are a qualified candidate and need assistance or an accommodation, please let us know by completing form.
Equinix is an Equal Employment Opportunity and, in the U.S., an Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to unlawful consideration of race, color, religion, creed, national or ethnic origin, ancestry, place of birth, citizenship, sex, pregnancy / childbirth or related medical conditions, sexual orientation, gender identity or expression, marital or domestic partnership status, age, veteran or military status, physical or mental disability, medical condition, genetic information, political / organizational affiliation, status as a victim or family member of a victim of crime or abuse, or any other status protected by applicable law.
We use artificial intelligence in our hiring process. Learn more here.
This posting is a new position within our organization.
Salary Context
This $186K-$280K range is above the 75th percentile for AI Product Manager roles in our dataset (median: $190K across 118 roles with salary data).
View full AI Product Manager salary data →Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 3,057 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At Equinix, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills Required
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $213,800 based on 518 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($233K) sits 9% above the category median. Disclosed range: $186K to $280K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Equinix AI Hiring
Equinix has 4 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Product Manager. Positions span Chicago, IL, US, Dallas, TX, US, Redwood City, CA, US. Compensation range: $212K - $300K.
Location Context
Across all AI roles, 17% (513 positions) offer remote work, while 2,528 require on-site attendance. Top AI hiring metros: New York (2,449 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 median).
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
AI Hiring Overview
The AI job market has 3,057 open positions tracked in our dataset. By seniority: 94 entry-level, 1,467 mid-level, 1,148 senior, and 348 leadership roles (Director, VP, C-Level). Remote roles make up 17% of the market (513 positions). The remaining 2,528 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
The AI Job Market Today
The AI job market spans 3,057 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,189), Data Scientist (233), AI Software Engineer (195). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (94) are outnumbered by mid-level (1,467) and senior (1,148) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 348 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 17% of all AI roles (513 positions), with 2,528 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,566 postings), Aws (974 postings), Azure (725 postings), Rag (683 postings), Gcp (597 postings), Prompt Engineering (472 postings), Pytorch (461 postings), Claude (447 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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