AI Engineer

• This job is responsible for providing leadership, technical direction and oversight to a team delivering technology solutions.
• Key responsibilities of the job are to provide oversight of the design, implementation, and maintenance of complex computer programs, align technical solutions to business objectives, and ensure that coding practices/quality comply with software development standards.
• Job expectations include conducting multiple software implementations and applying both depth and breadth in knowledge of several technical competencies.
• This role is for a Feature Lead in the Generative AI Core Engineering Team, responsible for designing and delivering high-performance, scalable, and secure APIs to support Generative AI workloads.
• The Feature Lead will collaborate with cross-functional teams to ensure solutions meet business objectives while adhering to technical standards.

Feature Lead – Technology, GenAI Platform

• Define end-to-end architecture for agentic AI-enabled platform across data, AI, orchestration, and integration layers with some real hands-on experience doing POCs
• Design and govern agentic orchestration framework for multi-step production workflows
• Establish architecture patterns for - RAG and grounding, Vector search and retrieval, MCP tool access layer, prompt management and evaluation
• Have a deep understanding of Agentic coding and best practices of using Agentic coding for large scale implementations
• Familiarity in implementing A2A or similar frameworks in a large scale environment
• Define integration architecture across - Lakehouse, ODS, document systems, Underwriting systems and third-party APIs
• Design configurable, metadata-driven framework for multi-LOB onboarding
• Define API/microservices patterns (Python/.NET hybrid)
• Define where and how to use - GenAI vs deterministic logic, agentic workflows vs pipeline workflows
• Establish multimodal integration approach combining structured, unstructured, and external data
• Design prompt lifecycle, evaluation, and optimization strategy
• Define AI safety and guardrails (PII, hallucination control, policy constraints)
• Establish ModelOps and PromptOps frameworks
• Ensure explainability, auditability, and traceability of AI outputs
• Lead technical execution across AI, data, and platform teams
• Guide engineers (AI, data, full-stack) and ensure alignment with architecture
• Drive technical decisions and stakeholder communication

AI Architect

• Partner with product, engineering, data science, and business teams to integrate AI into core offerings across SPGI
• Work with executive leadership to prioritize AI investments and resource allocation.
• Engage with external stakeholders (partners, vendors, academia) to foster innovation and collaboration.
• Establish best practices for AI model development, deployment, and monitoring.
• Ensure ethical AI use, compliance with regulations, and mitigation of bias and risks.
• Develop policies for data privacy, security, and responsible AI adoption.
• Oversee AI pilot programs and scaling of successful initiatives.
• Define KPIs to measure AI impact on business performance.
• Drive AI adoption across the organization through training and change management.
• Establish monitoring frameworks for AI system to system performance and drift analysis.
• Develop governance models for ethical AI agent deployment and compliance.

Associate Director – AI Engineering

• Lead the development, deployment, and ongoing optimization of AI and Generative AI solutions
• Implement engineering best practices to ensure scalability and uptime
• Track and report on platforms performance and availability metrics
• Be involved in all stages of the model development lifecycle from design to development, testing, and deployment
• Collaborate with software engineers, data scientists, and DevOps teams to design and implement technical designs and solutions
• Stay up-to-date with new technologies and industry trends

Lead AI Engineer

• You will architect and implement the core AI pipeline that powers Accel's test creation system.
• Work closely with the founder to design and build an AI-powered content generation system from the ground up.
• You'll contribute to meaningful parts of the product end-to-end from how the system ingests and understands source material, to how it produces and validates outputs, to how instructors interact with and review what the system generates.
• On the engineering side, you'll build and iterate on LLM-driven pipelines, work with retrieval and embedding techniques to ground outputs in real source material and develop backend services and APIs that tie everything together.
• Beyond pure coding, you'll be expected to think about output quality and building evaluation steps, catching failure modes, and improving the system based on real instructor feedback.
• You'll research new tools and techniques as the AI space evolves and bring relevant ideas directly into the product.
• This is a generalist role at an early-stage product where you'll wear multiple hats, work with ambiguity, and have direct input into how things are built.

AI Engineer, Internship – Intelligent Question Bank Platform

• Turn quantitative prototypes into reusable tools, services, packaged desktop applications, interfaces, and workflow products that can move from internal demo to client pilot to scaled offer.
• Ship across both cloud-hosted services and locally distributed desktop applications, including Electron-based apps when the workflow or client environment calls for it.
• Build enterprise hardening into the productization layer, including authentication, role-based access control (RBAC), observability, security, release quality, cost controls, and deployment discipline.
• Build evaluation, regression, and release discipline into the productization layer so model logic and agent behavior remain measurable as systems change.
• Work closely with the quant lead so model logic, evaluation intent, and governance requirements survive the move into production.
• Make pragmatic architecture choices across large language models (LLMs), deterministic rules, and hybrid systems based on value, latency, cost, and reliability.
• Help shape repeatable build patterns so strong prototypes become faster, more reliable, and more reusable over time.

QuantAI Engineer – Trading, Investment & Optimization

• Define architectural vision and solution aligned to enterprise architecture strategy
• Describe solution intent and evaluate system impacts
• Assist with end-to-end solution design and define non-functional requirements
• Deploy vendor AI platforms in an on-prem OpenShift environment
• Integrate AI capabilities across enterprise ecosystem
• Apply Responsible AI and governance standards
• Build and test integrations between AI platforms and enterprise systems

Architect – AI Platforms Implementation and Integration

• Partner deeply with the business organization to understand their data, workflows, operator behavior, and where AI can drive measurable improvement in throughput, quality, and customer outcomes.
• Architect and lead the development of foundational and agentic AI capabilities for the partner domain—applying modern modeling techniques, retrieval-augmented architectures, and agentic patterns at enterprise scale.
• Design long-running and multi-step agents that reason over rich enterprise context to deliver measurable business outcomes within a regulated environment.
• Build for production from day one —model evaluation harnesses, drift detection, monitoring, and deterministic deployment patterns that meet the bank's Model Risk Management and AI governance bar.
• Operate across the data and modeling application boundary —deploying models into the modeling environment while consuming data and context from the regulated data platform, in close coordination with the platform team.
• Influence architecture across two organizations —serving as the senior AI voice in the partner team's technical forums and as the domain voice in Cognitive AI Solutions architectural discussions.
• Mentor and uplevel engineers in both organizations on agentic AI, foundational modeling, and applied AI engineering practices.
• Translate technical progress into executive narrative —supporting demos and presentations to senior leadership and partner stakeholders.

Principal Applied AI Engineer

• Design and build multi-agent orchestration patterns (master-orchestrator + specialized worker agents) using protocols such as MCP and A2A
• Lead end-to-end delivery of agentic underwriting and claims automations, from prototype through production hardening
• Partner with IE on orchestration entry-point design (e.g., Azure Function endpoints, master-agent gateways) so the AE/IE seam is clean and scalable
• Lead offshore engineers and team members on agentic patterns, prompt engineering, and reliability practices
• Establish coding, evaluation, and observability standards for agentic systems within the AI & Automation Center of Excellence
• Translate business intent into working agentic systems — not just systems that compile, but systems that deliver measurable business outcomes.

AVP, AI Engineering

• Build scalable, robust ingestion pipelines for processing clickstream data, handling both participant-level (full-day employee activity) and case-level (end-to-end case lifecycle) data streams.
• Design and implement streaming platform-based orchestration for pipeline coordination, ensuring reliable data flow and processing guarantees.
• Deploy and manage containerized services on enterprise container platforms, implementing CI/CD pipelines and infrastructure-as-code practices.
• Implement comprehensive observability for LLM and VLM pipelines, including: Performance monitoring and metrics collection, Distributed tracing for multi-model pipelines, Logging and alerting for model inference, Cost tracking and optimization.
• Build fault-tolerant systems with retry mechanisms, circuit breakers, dead letter queues, and graceful degradation patterns.
• Work with VLMs to process clickstream video data and generate high-quality transcripts for downstream analysis.
• Ensure seamless integration with downstream Analysis & Evaluation workstream.

• Design, build, and maintain data pipelines that feed AI Accelerator projects, including ETL workflows, data transformations, and integration with internal and external APIs and data sources.
• Develop and manage relational databases (PostgreSQL/RDS), vector databases (OpenSearch, Milvus, Amazon S3 Vectors), and knowledge graphs (Amazon Neptune, Neo4j).
• Build and operate data infrastructure on AWS, leveraging services such as S3, Athena, RDS, ElastiCache (Redis), and Fargate.
• Build and maintain multi-agent systems using LangGraph, including state management, fan-out/fan-in orchestration, and subgraph composition.
• Develop tool integrations and MCP (Model Context Protocol) servers using FastMCP to enable agent interoperability.
• Implement LLM-powered workflows integrating foundational models (OpenAI, Anthropic, Gemini) via AWS Bedrock and direct API access.
• Build evaluation and observability pipelines using tools such as LangSmith or promptfoo.
• Write and maintain automated test suites (unit, integration, end-to-end) using pytest to ensure reliability of data pipelines and agent workflows.
• Develop evaluation frameworks for LLM outputs, including reference-based metrics and structured output validation.
• Implement data quality checks and validation at system boundaries to catch issues early in the pipeline.
• Support test-driven development practices and contribute to CI test automation.
• Partner with AI Engineers, Pod Leads, and Research scientists to deliver AI Accelerator projects within 12-week cycles.
• Participate in code reviews, design discussions, and technical problem-solving with pod teams.

• Design, build, and deploy generative and agentic AI solutions that automate, optimize, and transform tools supporting Medtronic’s global operations
• Collaborate closely with cross-functional partners to implement intelligent automation, architect AI-powered workflows, and develop scalable, robust APIs and platforms that drive operational efficiency and enhance staff experience across global facilities
• Support Medtronic’s strategic objectives by leveraging advanced AI / Machine Learning (ML) frameworks and modern development practices to deliver measurable business impact
• Ensure contributions align with the company's mission to alleviate pain, restore health, and extend life

Principal Artificial Intelligence Engineer

• Lead the design, development, and scaling of AI-driven pipelines to transform workflows across S&P Global.
• Partner with product, engineering, data science, and business teams to integrate AI into core offerings across SPGI.
• Work with executive leadership to prioritize AI investments and resource allocation.
• Engage with external stakeholders (partners, vendors, academia) to foster innovation and collaboration.
• Establish best practices for AI model development, deployment, and monitoring.
• Ensure ethical AI use, compliance with regulations, and mitigation of bias and risks.
• Develop policies for data privacy, security, and responsible AI adoption.
• Oversee AI pilot programs and scaling of successful initiatives.
• Define KPIs to measure AI impact on business performance.
• Drive AI adoption across the organization through training and change management.
• Establish monitoring frameworks for AI system to system performance and drift analysis.
• Develop governance models for ethical AI agent deployment and compliance.

• Lead engineering teams effectively, fostering a collaborative and high-performance culture
• Oversee timelines, team allocation, risk management, and task prioritization
• Mentor and support team members' professional growth, conduct performance reviews, and provide feedback
• Evaluate and enhance engineering processes, tools, and methodologies
• Collaborate with business partners and stakeholders to translate requirements into technical solutions
• Enforce technology standards and implement robust testing practices
• Accountable for shaping and scaling the Investment Banking and Lending AI platform
• Responsible for building scalable, performant, and accurate AI solutions
• Manage and track financial budgets for the area
• Build complex models using AI technologies
• Expertise in Data Science/ML with Python and Deep Learning libraries

Head of Banking AI Platform

• Build and improve AI-powered applications and internal tools
• Rapidly prototype, experiment, and iterate across client and internal projects
• Implement workflows that use LLMs, retrieval systems, agents, and structured outputs
• Contribute to shared platform work and reusable tooling
• Turn prototypes into reliable, client-ready systems
• Conduct research, benchmarking, testing, and evaluation of new approaches
• Own smaller projects or sub-projects with guidance from the team
• Take ownership of deliverables and help deliver work that meets client needs
• Write and maintain clear internal documentation

• As Director, AI Engineering and Enablement you will lead the architecture, design, and implementation of scalable Enterprise AI applications.
• Drive the technical strategy for Gen AI solutions, ensuring robust integration, security, and performance across the organization.
• Architect, design, and build end-to-end GenAI applications.
• Define solution architectures for leveraging cloud platforms (AWS, Azure, GCP) for scalability, reliability, and security.
• Present and propose technical solutions that fit requirements.
• Plan and coordinate project deliveries to ensure timelines and business needs are met.
• Develop core features and infrastructure; write production-grade code in Python, React, and other languages as needed.
• Lead implementation of best software engineering practices (modularity, version control, CI/CD, automated testing).
• Perform code and architecture reviews to ensure robust, maintainable, and high-performing solutions.
• Drive adoption of engineering best practices across the organization.
• Research, design, and implement Agentic AI solutions using frameworks such as LangChain and LangGraph.
• Build autonomous genAI agents capable of reasoning, multi-step workflows, and tool-usage.
• Optimize agent performance, reliability, and compliance for enterprise production. Evaluate emerging AI techniques, tools, and frameworks; make strategic recommendations.
• Collaborate with UX/UI teams to drive adoption and usability of AI applications.
• Maintain and evolve RESTful APIs and microservices architecture.

Director, AI Engineering – Enablement

• Build and evolve AI automations and agent-based workflows
• Design scalable agent architecture and orchestration patterns
• Suggest and implement AI-driven automation enhancements
• Act as an internal AI advocate by providing training and mentorship
• Partner with senior engineers and cross-functional teams
• Participate in the full AI SDLC, including design sessions

Senior AI Engineer

• Design, develop, and deploy scalable AI solutions
• Build LLM-based solutions for enterprise applications
• Integrate LLMs with business systems
• Architect AI agents for intelligent automation
• Implement CI/CD pipelines for machine learning models
• Monitor model performance and governance
• Collaborate with cross-functional teams for solution delivery
• Create technical documentation and mentoring

• Drive the development of the “Agent Ecosystem” by designing, building, and operationalizing enterprise-grade AI agents
• Serve as a player-coach, balancing hands-on engineering with strategic guidance
• Initially focus on creating foundational patterns and frameworks for scalability and reusability
• Design Agents and implement an agent orchestration layer
• Define and apply enterprise agent patterns
• Establish observability/monitoring for agents
• Integrate Microsoft Azure services

AI Developer

• Design, develop, and deploy machine learning and AI models for business use cases
• Design and fine-tune the underlying models, including reasoning chains, tool use, and agent architectures (ReAct, multi-agent frameworks, etc.)
• Define agent capabilities, scope autonomous decision-making boundaries, and manage stakeholder expectations around reliability
• Design / optimize prompt instructions, personas, tool descriptions, and reasoning scaffolds that guide agent behavior
• Develop and orchestrate multi-step AI agents capable of reasoning, tool usage, and workflow execution
• Build agentic workflows leveraging foundation models for task automation and decision support
• Test AI agents against enterprise systems, APIs, tools, and data platforms
• Design benchmarks and automated evaluations to measure agent reliability, accuracy, and safety across diverse scenarios
• Work with structured and unstructured data for predictive and generative AI solutions
• Manage and mentor a team of Data Scientists, setting clear expectations for model quality, documentation, and production readiness
• Collaborate with other data scientists, BI teams, and business stakeholders to translate requirements into AI-driven solutions
• Optimize model performance, scalability, and cost efficiency
• Monitor deployed models/agents and implement feedback loops and retraining strategies
• Document technical designs, agent workflows, and system architecture

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Feature Lead – Technology, GenAI Platform

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Associate Director – AI Engineering

Lead AI Engineer

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QuantAI Engineer – Trading, Investment & Optimization

Architect – AI Platforms Implementation and Integration

Principal Applied AI Engineer

AVP, AI Engineering

Lead AI Engineer

AI Engineer

Principal Artificial Intelligence Engineer

Associate Director – AI Engineering

Head of Banking AI Platform

AI Engineer

Director, AI Engineering – Enablement

Senior AI Engineer

AI Engineer

AI Developer

AI Engineer