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About This Role
Job Code
L2426IT
Department Name
IT Infrastructure
Reports To
Director, IT Infrastructure Strategy
FLSA Status
Exempt
Union Code
N/A
Management
Yes
About Us:
Building Services 32BJ Benefit Funds (“the Funds”) is the umbrella organization responsible for administering Health, Pension, Retirement Savings, Training, and Legal Services benefits to over 185,000 SEIU 32BJ members. Our mission is to make significant contributions to the lives of our members by providing high quality benefits and services. Through our commitment, we embody five core values: Flexibility, Initiative, Respect, Sustainability, and Teamwork (FIRST). By following our core values, employees are open to different and new ways of doing things, take active steps to improve the organization, create an environment of trust and respect, approach their work with the intent of a positive outcome, and work collaboratively with colleagues.
The Funds oversees and manages $11 billion of dollars in assets, which are made up of many, varied and complex funds. The dollars come from a number of sources, including the property owners who pay into the funds on behalf of their employees, and as such, requires those who oversee and manage the money to be highly skilled financial management people.
32BJ Benefit Funds will continue to drive innovation, equity, and technology insights to further help the lives of our hard-working members and their families. We use cutting edge technology such as: M365, Dynamics 365 CRM, Dynamics 365 F&O, Azure, AWS, SQL, Snowflake, QlikView, and more.
Please take a moment to watch our video to learn more about our culture and contributions to our members: youtu.be/hYNdMGLn19A
Job Summary:
The Manager, AI Platform & Agentic Orchestration is a hands-on technical leadership role responsible for building, configuring, operating, and governing the infrastructure that enables enterprise AI and agentic capabilities across Building Service 32BJ Benefit Funds (the "Funds"). The position focuses on the technical foundation required to safely deploy AI platforms, agents, models, skills, integrations, guardrails, and related services at scale. Rather than serving primarily as a business-facing solutioning role, this position owns the operational architecture and day-to-day management of the Funds' AI platform environment, including agentic infrastructure, model deployment, skill deployment, MCP server management, configuration standards, access controls, monitoring, usage management, and cost controls. The Manager, AI Platform & Agentic Orchestration works closely with IT Infrastructure, Security, Development, Data Strategy, Data Governance, and Compliance to ensure that AI capabilities can be implemented securely, reliably, and cost-effectively while protecting participant data and maintaining alignment with HIPAA, HITECH, DOL cybersecurity guidance, and internal policies. The role is expected to collaborate with technical and security leaders, including security counterparts such as the Information Security team, to ensure appropriate controls, guardrails, and incident-response practices are built into the AI operating environment.
Essential Duties and Responsibilities:
- Own the technical configuration, administration, and operational management of the Funds' enterprise AI and agentic infrastructure, including platform settings, environments, integrations, access controls, logging, and monitoring.
- Configure and manage agentic AI platforms and operating environments, including an AI-driven Software Development Lifecycle (SDLC) automation platform, ensuring that settings, permissions, guardrails, connectors, and deployment standards are implemented consistently and securely.
- Evaluate AI coding and agent tools such as Claude, GitHub Copilot, OpenAI/Codex, and Databricks Genie for use by the organization to address business needs.
- Deploy, configure, and maintain AI models, agents, skills, prompts, workflows, and related services across approved enterprise platforms such as Microsoft 365 Copilot, Azure OpenAI, Azure Databricks (Omnigent, Genie), Microsoft Foundry, Claude, and other sanctioned AI tooling.
- Manage MCP servers, agent-to-system integrations, API connections, knowledge sources, tool access, and other technical components required to enable secure agentic workflows.
- Implement technical guardrails, including role-based access, data-loss-prevention controls, prompt and tool restrictions, model-use boundaries, approval workflows, audit logging, and monitoring alerts.
- Establish usage, performance, reliability, and cost-management practices for AI infrastructure, including token consumption, licensing, compute utilization, environment sprawl, and vendor/platform cost controls.
- Collaborate with Infrastructure, Development, Security, and Data teams to ensure AI services are deployed within approved technical architectures and integrated appropriately with existing enterprise systems.
- Translate AI governance requirements into enforceable technical controls, including platform configuration standards, approved deployment patterns, restricted-use settings, and automated or manual approval checkpoints.
- Develop and maintain enterprise AI technical standards and reference architectures; participate in AI architecture reviews and the AI Center of Excellence; and contribute to the enterprise AI technology roadmap.
- Maintain the technical AI inventory, and lifecycle of deployed models, agents, prompts, skills, MCP servers, connectors, knowledge sources, integrations, and environments, including owners, access levels, versioning, monitoring, change management, retirement, and cost profiles.
- Conduct or support pre-deployment technical risk reviews focused on PHI/ePHI exposure, data residency, storage and retention, system integration risk, model/tool access, logging, auditability, and controls against prompt injection, data leakage, and unauthorized tool use.
- Operationalize the Funds' AI Tool Vetting and Approval process, administering the intake queue, applying the risk-tiered classification model (Low / Medium / High / Prohibited), and routing requests for appropriate review. Low request items will be deployed without further review.
- Partner with Information Security, the vCISO, and Compliance to ensure AI infrastructure aligns with cybersecurity standards, incident-response processes, HIPAA/HITECH requirements, and the Funds' internal control environment.
- Support evaluation of third-party AI platforms and infrastructure components, including technical security requirements, data-use terms, model training restrictions, retention practices, logging capabilities, breach-notification obligations, and integration risks.
- Monitor deployed AI services for unauthorized data exposure, policy violations, cost anomalies, model or agent misuse, integration failures, and drift from approved technical configurations.
- Document control decisions, configuration standards, mitigation requirements, exceptions, and remediation plans in coordination with governance, security, and infrastructure leadership.
- Develop and maintain technical standards, runbooks, deployment procedures, configuration checklists, and operating practices for AI platforms, agents, models, skills, MCP servers, and related services.
- Produce operational dashboards and reports covering usage, licensing, infrastructure cost, token or compute consumption, platform utilization, deployed agents and skills, open risks, exceptions, and remediation status.
- Serve as the technical implementation lead for AI infrastructure initiatives, ensuring the Funds has the platform capacity, controls, and deployment mechanisms needed to support a growing portfolio of AI projects.
- Partner with business-facing teams as needed to understand implementation requirements, while keeping primary accountability for the technical environment, deployment architecture, and operational controls.
- Partner with Enterprise Architecture to ensure AI platforms, services, and integrations align with enterprise technology standards and architecture.
- Participate in AI governance discussions by providing technical feasibility assessments, infrastructure readiness updates, cost implications, control recommendations, and implementation constraints.
- Stay current on emerging AI platform architectures, agentic infrastructure patterns, MCP standards, AI security controls, and responsible deployment practices, ensuring the Funds' technical environment remains secure and scalable.
- Perform other duties as assigned by management.
- Provide support outside of regular working hours and on weekends. Qualifications (Competencies):
- 5+ years of experience in IT infrastructure, cloud engineering, platform administration, systems integration, DevOps, cybersecurity, data platforms, or enterprise technology operations.
- Hands-on experience deploying, configuring, operating, or securing enterprise AI, automation, cloud, integration, or data platforms is strongly preferred.
- Experience with vendor/platform management, technical security reviews, operational monitoring, cost management, and production support practices.
- Healthcare, benefits administration, HIPAA-regulated, or highly regulated enterprise experience preferred.
- Strong hands-on technical knowledge of enterprise AI platforms, generative AI services, agentic architectures, model deployment patterns, skill deployment, API integrations, and secure platform operations.
- Working knowledge of AI/ML concepts and AI-specific risks, including prompt injection, data leakage, unauthorized tool use, model drift, hallucination risk, explainability, and output reliability.
- Experience configuring and operating AI platforms, cloud-based AI services, Microsoft 365 Copilot, Azure OpenAI, MCP servers, agent frameworks, or comparable enterprise AI infrastructure.
- Working knowledge of prompt engineering and versioning, model and prompt lifecycle management, agent orchestration, MCP architecture, RAG and vector technologies, AI evaluation and observability, Git/version control, APIs, Python, Azure AI Foundry/Azure OpenAI, and AI governance and guardrail technologies.
- Strong understanding of cybersecurity, access controls, logging, monitoring, incident response, data-loss prevention, and secure integration practices.
- Understanding of data privacy and regulatory compliance requirements, particularly HIPAA and HITECH, as well as familiarity with DOL cybersecurity guidance and emerging AI governance expectations.
- Ability to manage AI infrastructure costs, licensing, token/compute consumption, utilization reporting, vendor/platform usage, and environment governance.
- Demonstrated ability to create technical standards, runbooks, configuration documentation, operating procedures, and deployment controls.
- Strong collaboration skills with technical infrastructure, development, security, compliance, privacy, and data governance stakeholders.
- Clear communication skills, with the ability to explain technical risks, controls, costs, and implementation requirements to both technical and non-technical stakeholders.
- AI platform administration and agentic infrastructure operations.
- Model, agent, skill, and MCP server deployment.
- Technical guardrails, access controls, and secure configuration management.
- AI infrastructure monitoring, usage reporting, and cost management.
- Cybersecurity, privacy, and compliance alignment, including HIPAA/HITECH.
- Technical documentation, runbooks, deployment standards, and operational discipline.
- Vendor/platform evaluation and third-party technical risk review.
- Collaboration with infrastructure, development, security, compliance, and data governance teams.
- Problem-solving, systems thinking, and implementation-focused execution. Soft Skills (Interpersonal Skills):
- Detail oriented with excellent organization and analytical skills.
- Ability to plan, take initiatives to accomplish objectives in a timely fashion, and work independently.
- Ability to prioritize work and meet deadlines.
- Ability to establish and maintain effective working relationships with project team members, supervisors, and other employees. Education:
Bachelor's degree in Information Technology, Computer Science, Cybersecurity, Data Engineering, Cloud Engineering, or a related technical field; relevant certifications or a Master's degree preferred.
Language Skills:
Speak, read, write and understand English
Reasoning Ability:
High
Certificates, Licenses, Registrations:
Certifications such as Microsoft Azure Administrator, Azure AI Engineer, Azure Security Engineer, CISSP, CISM, CRISC, cloud architecture, DevOps, cybersecurity, or AI governance/responsible-AI credentials are beneficial. Project-management certification is a plus but not required.
Shift
Standard business hours, with after-hours, weekend, and on-call support as required.
Physical Demands:
The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals to perform the essential functions.
Work Environment:
The work environment characteristics described here are representative of those an employee encounters while performing the essential functions of this job. Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions.
Salary Context
This $175K-$200K range is above the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Building Service 32BJ Benefit Funds, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($187K) sits 12% below the category median. Disclosed range: $175K to $200K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Building Service 32BJ Benefit Funds AI Hiring
Building Service 32BJ Benefit Funds has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $200K - $200K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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.
Frequently Asked Questions
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