Top Artificial Intelligence Jobs

Leads enterprise business intelligence strategy, roadmap, product delivery, semantic modeling, governance, adoption, and AI-assisted analytics. Partners with Data Engineering, Data Governance, Data Science, AI Solutions, and IT to deliver scalable Power BI and Snowflake capabilities. Manages BI demand, stakeholder relationships, technical standards, team development, and business-value measurement while remaining hands-on in solution design and troubleshooting.

Leads the design, development, deployment, and governance of enterprise-grade Python-based generative AI platforms and services. Defines architecture with technical, data science, and business stakeholders; drives production implementations, testing, monitoring, scalability, and risk controls. Manages complex initiatives and vendor partnerships while advising developers and analysts. Requires strong Python, AI/ML, cloud, CI/CD, software engineering, leadership, communication, and project management expertise.

Leads enterprise business intelligence strategy, roadmap, product delivery, semantic modeling, governance, adoption, and AI-enabled analytics. Partners with Data Engineering, Data Governance, Data Science, AI Solutions, and IT to build scalable Power BI capabilities. Manages BI demand, stakeholder relationships, technical standards, and a BI team while coaching contributors and guiding solution design. Uses performance, adoption, reliability, and business-value metrics to improve analytics products.

Leads development of AI/ML research platforms connecting multimodal data, knowledge graphs, scalable compute, foundation models, and reusable tools. Builds agentic AI frameworks, RAG pipelines, and scientific workflows while overseeing software architecture, testing, CI/CD, reproducibility, security, and maintainability. Partners with scientists and technology teams on cloud, Databricks, GPU, and high-performance computing environments. Remains hands-on in architecture and implementation while mentoring engineers and enabling biomedical research applications.

Leads the strategy, roadmap, governance, and delivery of enterprise business intelligence capabilities. Owns BI product experience, semantic models, reporting standards, demand prioritization, adoption metrics, and AI-assisted analytics initiatives. Partners with Data Engineering, Data Governance, Data Science, AI Solutions, and IT on architecture, security, and scalable delivery. Manages, coaches, and develops the BI team while translating business needs into measurable BI products and outcomes.

Owns the vision, roadmap, backlog, demand, funding, and lifecycle for enterprise AI and data platform products. Partners with business stakeholders, executives, architects, engineering teams, analysts, and vendors to define priorities, user stories, releases, KPIs, governance, and platform improvements. Oversees products including Snowflake, Dataiku, Coalesce, Palantir, and data ingestion services while supporting data quality, privacy, cybersecurity, access management, platform stability, and product delivery processes.

Design, build, and scale enterprise LLM-powered applications and multi-agent systems using ADK and LangChain. Develop AI pipelines, API integrations, guardrails, observability, and production deployment frameworks. Partner with Risk, Compliance, and Model Governance teams to meet financial services regulatory requirements, lead end-to-end delivery, establish engineering standards, and mentor technical teams.

Design, build, and scale enterprise-grade LLM and agent-based systems using ADK, LangChain, RAG, and tool orchestration. Develop guardrails, AI pipelines, API integrations, monitoring, and performance improvements while meeting financial-services risk, compliance, and model-governance requirements. Lead production delivery, establish engineering standards, and mentor teams in partnership with Technology, Product, Risk, and Compliance.

Leads enterprise business intelligence strategy, product roadmap, semantic modeling, governance, delivery standards, and AI-enabled analytics. Oversees Power BI capabilities, partners with Data Engineering, Data Governance, Data Science, AI Solutions, and IT, and manages BI demand, adoption, performance, and business value. The role leads and develops the BI team, guides technical design, translates business needs into scalable products, and supports self-service and personalized insights.

Leads the architecture, development, governance, and production deployment of enterprise Generative AI and agentic AI systems. Responsibilities include mentoring AI engineers, defining technical strategy, partnering with business stakeholders, establishing responsible AI and MLOps practices, developing machine learning and deep learning solutions, and overseeing containerized deployments. The role requires hands-on expertise in LLMs, RAG, multi-agent systems, Python, AI frameworks, data pipelines, monitoring, and scalable production services.

Develop and maintain scalable Python applications for Generative AI, integrating APIs such as OpenAI and Gemini and implementing RAG systems. Build backend services with Python frameworks, optimize application performance and security, write unit and integration tests, participate in code reviews, and collaborate with product, data science, and engineering teams. The role also involves application lifecycle management, user support, risk assessment, mentoring, and advising senior stakeholders.

Leads the strategy, roadmap, delivery, governance, and adoption of enterprise business intelligence capabilities. Oversees BI products, semantic models, reporting standards, demand prioritization, and AI-assisted analytics in partnership with Data Engineering, Data Governance, Data Science, AI Solutions, and IT. Manages and develops the BI team, guides technical design, measures business value, and translates organizational needs into scalable analytics products.

Develop and maintain scalable Python applications for Generative AI, integrating APIs such as OpenAI and Gemini and implementing RAG systems. Build backend services with FastAPI, Flask, or Django; write tests; optimize performance, scalability, and security; and collaborate with product, data science, and engineering teams. The role also involves application lifecycle oversight, user support, code reviews, risk assessment, mentoring junior analysts, and advising stakeholders.

Leads enterprise business intelligence strategy, product roadmaps, semantic modeling, reporting standards, adoption, and AI-assisted analytics. Partners with Data Engineering, Data Governance, Data Science, AI Solutions, and IT to deliver scalable, secure, governed BI capabilities using Power BI and Snowflake. Manages demand prioritization, stakeholder relationships, performance measurement, and a BI team while remaining technically engaged in solution design and delivery.

Leads application systems analysis and programming while designing scalable Generative AI applications in Python. Develops GenAI API integrations, RAG systems, and backend services using Python web frameworks. Collaborates with product managers, data scientists, and engineers; establishes coding, testing, deployment, and architecture standards; improves performance, scalability, and security; and mentors developers and analysts. Requires strong software engineering practices, testing, databases, CI/CD, and hands-on Generative AI experience.

Leads enterprise business intelligence strategy, roadmap, product delivery, semantic modeling, governance, adoption, and AI-enabled analytics. Partners with Data Engineering, Data Governance, Data Science, AI Solutions, and IT to deliver scalable Power BI and Snowflake capabilities. Manages BI demand, prioritization, standards, stakeholder relationships, and a technical team while guiding solution design and performance. Drives trusted reporting, self-service analytics, personalized insights, and measurable business value across corporate and community audiences.

Leads enterprise business intelligence strategy, roadmap, product delivery, semantic modeling, governance, and AI-enabled analytics. Manages the BI team, partners with Data Engineering, Data Governance, Data Science, AI Solutions, and IT, and ensures scalable reporting across Power BI, Snowflake, and related platforms. Translates business needs into BI products, measures adoption and value, establishes delivery standards, and coaches technical professionals while remaining involved in solution design and complex initiatives.

Own the roadmap and outcomes for Credo AI’s AI governance solutions, taking research prototypes through validation, beta, and general availability. Lead AI-powered intake, risk mapping, knowledge graph recommendations, vendor risk, and evaluation capabilities. Define product metrics and quality thresholds, test features hands-on, prototype with AI coding tools, engage customers, and partner with Sales, Marketing, Customer Success, and Engineering on packaging, launches, and adoption.

Designs, develops, deploys, and operates scalable AI and machine learning solutions across the enterprise. Responsibilities include building end-to-end ML infrastructure, managing LLM lifecycles, optimizing RAG systems and prompts, evaluating models, deploying Azure-based solutions, writing tested software, supporting CI/CD and DevSecOps, monitoring production systems, and collaborating with engineering, architecture, DevOps, and testing teams.

Design, develop, and deploy enterprise AI applications and agentic solutions using LLMs, RAG, intelligent automation, and machine learning. Build data pipelines, APIs, microservices, and scalable AI components with Python or Java. Collaborate with architects, product owners, researchers, and engineering teams in a regulated financial environment. Lead multiple AI use cases, oversee AI engineers within a Center of Excellence, document implementations, and evaluate emerging AI technologies.

Develop production-grade AI agents and intelligent automation using Python, tool integrations, RAG pipelines, structured outputs, guardrails, and resilient execution workflows. Build MCP servers and enterprise API connectors, implement testing and evaluation systems, and collaborate directly with business users to refine agent prototypes. The role also involves architectural decisions, peer reviews, agile delivery, coaching junior engineers, and ensuring security, compliance, and operational reliability.

Develop and maintain Python-based AI applications under senior guidance. Collaborate on requirements and feature implementation, use Pydantic for data validation, write documented code, participate in reviews, troubleshoot issues, and support testing and CI/CD. The role requires Python, object-oriented programming, REST APIs, and knowledge of LLMs, with opportunities to learn modern frameworks, backend development, data engineering, and cloud platforms.

Design and prototype AI-powered solutions that transform business processes. Partner with business and technology leaders to identify automation opportunities, translate business needs into technical requirements, develop AI agents and proof-of-concepts, measure impact, document solutions, train users, and support scalable adoption. The role requires hands-on expertise in LLMs, machine learning, NLP, data modeling, and strong communication with technical and nontechnical stakeholders.

Build and deploy production AI agents within business technology teams. Responsibilities include agent prototyping, prompt and data optimization, automated evaluation testbeds, benchmark dataset curation, CI/CD test automation, guardrail and security validation, telemetry monitoring, and diagnostic reporting. The role requires hands-on software engineering, collaboration in agile pods, and adherence to enterprise risk, compliance, and security standards.

Leads Citi’s generative and agentic AI delivery lifecycle, from roadmap execution and solution design through production deployment and support. Builds and manages global AI engineering teams, oversees enterprise-scale applications and multi-agent systems, and partners with data, cloud, and MLOps teams. Establishes CI/CD, LLMOps, governance, responsible AI, security, and quality standards while managing stakeholders, budgets, risks, and regulatory compliance.