Sr Agentic AI Architect Developer

About the Role:

Grade Level (for internal use):

12 S&P Global Corporate

The role: Sr AI Architect developer.

Location: NYC: 55 water street (2 days per week onsite)

Data Platform, Agentic AI & Developer Productivity

Role Overview

This role turns AI into a compounding capability for our data and engineering organization. The Strategic Lead owns the strategy, outcomes, and operating model for our data platform (Databricks and Microsoft Fabric), agentic AI products, and developer productivity, and is accountable for the measurable business value they deliver.

This is not a backlog-administration role. The Strategic Lead decides where AI and agents should be applied, defines how humans and agents work together safely, and uses AI daily to run discovery, specification, analysis, and reporting at a fraction of traditional overhead.

How this role differs from a traditional Product Owner / Product Manager ension

Traditional PO / PM

AI-Native Strategic Lead

Unit of value

Backlog items and sprint velocity

Business and engineering outcomes, measured and attributed

Primary artifact

User stories, BRDs, process flows

Working prototypes, evals, guardrail specs, outcome metrics

Relationship with AI

Manages products that contain AI

Builds with AI daily and designs how humans and agents work together

Delivery model

Ceremony-driven Scrum cadence

Continuous flow, small bets, rapid experiments, automated reporting

Quality lens

UAT against acceptance criteria

Continuous evaluation of agent quality, safety, cost, and latency

Risk lens

Dependencies and schedule

Responsible AI, data governance, auditability, and cost control

Key Responsibilities

Strategy and Outcome Ownership

Set the strategy: own the vision and outcome-based roadmap for the data platform, agentic AI capabilities, and developer productivity.

Measure outcomes, not outputs: define and track engineering cycle time, time-to-trusted-data, agent task success, adoption, and cost per outcome; report value realization to leadership.

Prioritize AI opportunities: identify where agents remove the most friction across the delivery and data lifecycles; build business cases and make build/buy/partner decisions; track the market and set scale-or-stop criteria for each bet.

Agentic Product and Platform Leadership

Define agent behavior: specify autonomy levels, tool and data access, guardrails, escalation paths, and human-in-the-loop checkpoints.

Own evaluation and quality: set eval suites and acceptance thresholds for accuracy, safety, latency, and cost; ensure agents are observable in production.

Shape the architecture: work with architects and engineers on orchestration, context and retrieval, tool integration, model selection, and agent identity and permissions.

AI-Ready Data Foundation

Partner daily with data engineering on Databricks and/or Fabric to make data products AI-ready: quality SLAs, data contracts, semantic definitions, and lineage.

Govern by design: use Unity Catalog, OneLake, and related controls so agents access only the data they should, with full auditability; define KPIs and dashboards with BI engineers.

AI-Accelerated Delivery and Developer Productivity

Compress discovery to delivery: use AI to prototype, draft and validate specifications, synthesize research, and analyze telemetry; automate status reporting and documentation.

Modernize ways of working: favor lightweight planning, small instrumented releases, and continuous flow over ceremony.

Lift engineering effectiveness: drive adoption of AI-assisted engineering and internal platform capabilities; measure impact with DORA, SPACE, and AI-specific metrics.

Responsible AI, Governance and Change

Establish practical AI governance with security, legal, and risk: approval of models and agents, data privacy, auditability, compliance, and cost guardrails.

Lead adoption: redesign workflows for human-agent teaming, run enablement and training, and align executives, engineers, and end users through clear communication and influence.

Required Skills and Qualifications

Experience: 6+ years in product, platform strategy, data/AI, or technical leadership, including 2+ years delivering AI/ML or LLM-based solutions into real use.

Hands-on agentic AI experience (must-have): has built, shipped, or led agentic or LLM solutions, with working fluency in prompting, retrieval, tool use, multi-agent orchestration, evaluation, guardrails, and cost/latency trade-offs.

AI as a daily working method: routinely uses AI to research, analyze, and prototype without waiting for engineering capacity.

Data platform fluency: hands-on experience with Databricks and/or Microsoft Fabric; comfortable discussing pipelines, data quality, modeling, and governance with data engineers.

Developer productivity: familiarity with CI/CD, observability, internal developer platforms, and measuring engineering effectiveness.

Strategic and commercial judgment: proven ability to define outcome-based roadmaps, build business cases, and demonstrate value.

Responsible AI and governance: working knowledge of AI risk, data privacy, auditability, and data governance and quality frameworks.

• •Influence and communication: executive presence; able to align senior stakeholders and explain AI concepts to non-technical audiences; pragmatic use of Agile and tools such as JIRA or Azure DevOps.
• •Education: Bachelor's degree in Computer Science, Information Systems, Engineering, Business, or a related field, or equivalent experience.

Preferred / Nice-to-Have:

Working knowledge of core data and AI engineering concepts (data pipelines, data modeling, RAG, agent orchestration, evals) and day-to-day experience working with the teams that build and run them.

Familiarity with agent and orchestration platforms (e.g., LangGraph, AutoGen, Databricks Mosaic AI, Azure AI Foundry, Copilot Studio).

Familiarity with Unity Catalog, Delta Lake, Fabric OneLake, and Power BI.

Experience leading change or transformation initiatives, ideally in regulated industries with compliance and audit requirements.

Product or agile certifications (e.g., CSPO, PMI-ACP) welcome but not required.

Compensation/Benefits Information (US Applicants Only):

S&P Global states that the anticipated base salary range for this position is $140,000 USD – $200,000 USD. Final base salary for this role will be based on the individual’s geographical location as well as experience and qualifications for the role.

In addition to base compensation, this role is eligible to receive additional S&P Global benefits. For more information on the benefits we provide to our employees, please click here .

What’s In It For You?

Our Mission:

Advancing Essential Intelligence.

Our People:

We're more than 35,000 strong worldwide—so we're able to understand nuances while having a broad perspective. Our team is driven by curiosity and a shared belief that Essential Intelligence can help build a more prosperous future for us all.From finding new ways to measure sustainability to analyzing energy transition across the supply chain to building workflow solutions that make it easy to tap into insight and apply it. We are changing the way people see things and empowering them to make an impact on the world we live in. We’re committed to a more equitable future and to helping our customers find new, sustainable ways of doing business. Join us and help create the critical insights that truly make a difference.

• Our Values:
• Integrity, Discovery, Partnership

Throughout our history, the world's leading organizations have relied on us for the Essential Intelligence they need to make confident decisions about the road ahead. We start with a foundation of integrity in all we do, bring a spirit of discovery to our work, and collaborate in close partnership with each other and our customers to achieve shared goals.

Benefits:

We take care of you, so you can take care of bu