Top Artificial Intelligence Jobs

Leads the credit union’s non-lending Operations organization, including contact center, member services, branches, virtual branch, deposits, payments, and card services. Owns reliable daily performance, member and employee experience, controls, compliance, risk, resilience, and operating results. Drives process redesign, modernization, analytics, automation, organizational development, and cross-functional initiatives while developing leaders and future-ready capabilities.

Supports generative AI adoption across investment banking and capital markets by developing GPTs, AI workflows, prompts, and emerging agent-based solutions. Provides user support, troubleshooting, training, documentation, data analysis, vendor evaluation, and innovation research. The role partners with technical and business stakeholders to pilot and scale AI use cases, maintain governance standards, create leadership materials, and improve banker workflows, adoption, and user experience.

Build and deploy production generative AI and agentic systems for enterprise clients. Responsibilities include identifying valuable customer problems, shaping technical architecture, developing multi-agent workflows and integrations, and owning systems through deployment and scale. The role requires hands-on ownership of evaluation, observability, security, reliability, latency, scalability, cost, and measurable business outcomes while collaborating directly with senior technical and business leaders.

Develop, deploy, and support secure, scalable production software and AI/ML solutions. Collaborate with product, UX, and engineering to deliver features, create tests and automation, instrument monitoring and dashboards, and participate in agile processes while continuously learning and improving team practices.

Senior cheminformatics professional responsible for building and maintaining scalable chemical databases, search systems, indexing, similarity analysis, and analytics workflows. The role develops agentic AI solutions for drug and pipeline analysis, integrates scientific literature with computational tools, and applies machine learning to pharmaceutical research and market analytics. Candidates need advanced chemistry or related scientific education, cheminformatics expertise, pharmaceutical or biotech experience, and strong interdisciplinary communication skills.

Provides technical leadership for designing, building, and operating secure, reliable, scalable AI products. Leads generative AI, agentic, retrieval, and evaluation systems from concept through production; defines architecture and engineering standards; evaluates emerging models and frameworks; partners cross-functionally; mentors engineers; and resolves complex technical risks and trade-offs.

Own product and feature launches, go-to-market communications, messaging, positioning, competitive battlecards, customer insights, content, and sales enablement for enterprise finance automation products. Coordinate with Product, Sales, Customer Success, and Demand Generation while translating complex AI capabilities into compelling messaging for finance buyers, including AP leaders and CFOs.

Develop and support secure AI-powered and web-based applications, APIs, integrations, and automation solutions. Build LLM applications using retrieval-augmented generation, vector search, embeddings, tool calling, and agentic workflows. Integrate enterprise systems and databases, apply secure software practices, support deployments, and partner with stakeholders to deliver maintainable production solutions.

Develop and deploy autonomous AI agent workflows, RAG systems, and LLM integrations for financial planning and analytics. Build forecasting models for revenue, EBITDA, and other metrics while ensuring workflows are auditable, explainable, scalable, and compliant with financial data governance standards. The role requires Python-based AI development, LLM orchestration, vector databases, prompt engineering, and translating financial business logic into production AI architectures.

Develop and deploy applied AI and machine learning systems for customer-facing agents. Responsibilities include model fine-tuning, reinforcement learning, evaluation design, large-scale text and audio data work, post-training, voice AI, long-horizon optimization, and production deployment. The role translates customer and product challenges into ML problems and carries solutions from experimentation through measurable customer impact.

Develop high-performance AI kernels and software for neuromorphic hardware. Optimize convolutional, transformer, and generative AI workloads using parallelism, sparsity, quantization, fusion, and related techniques. Build performance, power, and area models, reference implementations, benchmarks, validation tools, and regression infrastructure. Collaborate with hardware, compiler, runtime, and application engineers throughout the hardware-software lifecycle.

Build scalable infrastructure for LLM-based agentic AI supporting autonomous security operations. Responsibilities include designing agent orchestration frameworks, LLM inference and resource-management systems, MCP servers, supporting APIs, and distributed backend services. The role collaborates with machine learning, security research, engineering, and product teams. Required experience includes backend infrastructure, distributed systems, APIs, microservices, cloud deployment, containerization, orchestration, and Python, Java, or Go.

Develops applied machine learning, causal inference models, generative AI systems, and agentic tools for pharmaceutical R&D. Analyzes multimodal clinical, genomic, transcriptomic, pathology, molecular, and real-world data; collaborates with pharma partners and internal research and engineering teams; communicates scientific findings; and coaches computational biology and real-world evidence teams on AI adoption.

Design, build, deploy, and optimize production-grade machine learning and LLM systems, including RAG pipelines, agentic workflows, fine-tuning, embeddings, and chatbots. Own projects from architecture and prototyping through deployment, monitoring, and iteration. Collaborate directly with enterprise clients, mentor junior ML engineers, conduct code reviews, and help establish internal technical standards and best practices.

Build and deploy production GenAI and LLM systems for financial services and healthcare clients using Claude and AWS. Embed with customers to understand business functions, design solutions, create evaluation suites, and own delivery from strategy through production. Contribute to AI delivery frameworks, blueprints, and enterprise AI adoption while engaging senior stakeholders in pre-sales and technical scoping. The role requires hands-on software engineering, cloud-native delivery, autonomy, and comfort working in ambiguous environments.

Owns company-wide communications strategy, internal communication platforms, rollout timelines, newsletters, leadership meetings, and executive messaging. Manages company initiatives from assignment through completion, tracks accountability, coordinates leadership offsites, and improves organizational processes. The role also applies AI technologies to optimize operations, supports change management, and partners with stakeholders across the organization.

Lead the transformation of engineering workflows through AI-native process redesign, automation, and digital engineering solutions. Integrate AI capabilities into platforms such as Teamcenter, NX, Cameo, and Ansys; guide solution development using Agile, SDLC, and CI/CD practices; establish adoption, support, and performance metrics; and collaborate with engineering, digital technology, and information assurance teams in classified environments.

Conduct research on frontier AI detection and related topics under mentorship, selecting projects across adversarial robustness, interpretability, mixed authorship, new architectures, multimodal detection, AI-generated training data, and agentic AI detection. Interns are expected to pursue publishable research for a major AI conference and may propose original topics.

Conduct research and engineering for AI-generated content detection models. Responsibilities include training large-scale deep learning models, creating synthetic datasets, developing novel detection methods, researching adversarial robustness and interpretability, publishing technical work, deploying models, building infrastructure, and collaborating with engineering and product teams.

Engineer and administer enterprise AI platforms, integrations, access controls, security guardrails, and governance. Build MCP servers, API wrappers, automation, and lightweight services; manage deployment through infrastructure as code and CI/CD. Evaluate new AI capabilities, troubleshoot integrations, advise business users, implement telemetry, and identify opportunities for responsible AI adoption while balancing innovation, cost, security, and enterprise risk.

Leads enterprise workflow reinvention and systems-of-work design across business and technology teams. Designs human-AI collaboration, automation opportunities, controls, decision paths, and implementation-ready workflow artifacts. Facilitates senior stakeholder workshops, connects business intent to architecture and delivery, promotes reusable enterprise patterns, and helps establish systems design practices. Measures engagement impact through business outcomes, case studies, and executive storytelling.

Lead data and AI engineering initiatives, including large-scale data architecture, AI and generative AI solutions, roadmaps, and enterprise technical delivery. Partner with customers and cross-functional stakeholders to identify strategic opportunities, communicate technical value, and deliver solutions. Mentor data engineers, establish best practices, conduct code reviews, support business development, and apply cloud-native technologies across Azure, AWS, Databricks, Snowflake, and Palantir.

Leads the engineering, integration, deployment, and support of AI-powered security operations platforms. Designs security automation, SOAR integrations, RAG architectures, agent orchestration, evidence pipelines, observability, governance, and cloud-based security workflows. Partners across security, risk, data, and technology teams to improve investigations and reduce response times while managing AI-related risks. Provides technical leadership, establishes engineering standards, mentors staff, and communicates complex security risks to senior stakeholders.

Leads a technology and intellectual property transactions practice, negotiating complex commercial agreements and overseeing IP diligence for investments, acquisitions, divestitures, and spin-offs. Advises senior client stakeholders, develops transaction methodologies, recruits and mentors attorneys, and collaborates with engineers on legal automation and tooling. Requires extensive technology transactions experience, active bar membership, law firm and in-house backgrounds, and expertise in software licensing, cloud terms, data and AI provisions, open source, trade secrets, and IP representations.

Leads threat emulation, red teaming, penetration testing, vulnerability assessments, exploit proof-of-concept development, incident investigations, and security control enhancement. Analyzes attack patterns, logs, traffic, vulnerabilities, and emerging threats; recommends mitigations; communicates findings across IT and business teams; supports security initiatives; and mentors junior staff.