Top Artificial Intelligence Jobs

Leads the design, development, and deployment of AI-powered solutions, intelligent automation, and digital workers. Prototypes and scales generative and agentic AI capabilities, integrates them with enterprise platforms and workflows, and establishes AI engineering, governance, security, privacy, and responsible AI standards. The role also leads technical workstreams, mentors engineers, evaluates emerging technologies, and drives enterprise adoption of innovative AI solutions.

Own AI product roadmaps for finance and enterprise functions, translating business priorities into agent requirements, user stories, and deployment plans. Lead process discovery, redesign, sprint planning, stakeholder alignment, change management, training, governance, and ROI measurement. Partner with engineers and data architects to deliver production AI solutions, present results to executive stakeholders, support client growth initiatives, and mentor delivery team members.

Own architecture and direction for Suno’s AI Operations Engineering team. Build intelligent agents, workflows, internal tools, APIs, authentication patterns, and self-service deployment capabilities that help non-engineering teams scale. Evaluate third-party tools, integrate customer experience systems such as Zendesk, and partner with legal, finance, people, support, and content operations to identify and solve workflow challenges.

Build AI agents, workflows, internal tools, APIs, authentication patterns, and customer experience integrations that improve operations across support, legal, finance, people, and content teams. Evaluate build-versus-buy decisions, support self-service app deployment, and partner with stakeholders to identify automation opportunities. The role requires full-stack engineering experience and comfort working in an early-stage, rapidly evolving environment.

Lead AI transformation engagements for financial services executives as a fractional Chief AI Transformation Officer. Win executive business, diagnose where AI can create enterprise value, shape actionable transformation strategies, mobilize product and engineering teams to deliver outcomes, and expand long-term client relationships. The role combines executive advisory, applied AI strategy, engagement leadership, and commercial ownership.

Design and develop real-time voice-to-voice AI agents using LLMs, speech recognition, speech synthesis, streaming, and agent frameworks. Build Python and C#/.NET backend services, APIs, telephony integrations, and RAG workflows. Optimize latency, reliability, and conversational performance while evaluating emerging AI technologies and collaborating with product and engineering teams.

Design and deploy autonomous, multi-agent AI systems using agent orchestration frameworks, MCP, A2A, RAG, and knowledge graphs. Fine-tune and optimize language models for edge deployment, build evaluation and observability frameworks, and advance AI solutions from prototypes to production. The role requires cloud architecture experience, software development expertise, TS/SCI clearance with polygraph, and collaboration in ambiguous, rapidly changing environments.

Designs enterprise AI and agent architectures, including MCP servers, governed agent platforms, prompt harnesses, RAG and context-engineering systems. Authors architecture, security, SDLC, and governance artifacts; develops reusable AI accelerators; supports knowledge graph and ontology design; and creates enterprise architecture leadership materials. Acts as the technical liaison between enterprise architecture and service-line engineering teams, translating strategic decisions into practical implementation guidance.

Affiliate referral opportunity for well-connected professionals who can introduce qualified U.S.-based companies to Mode. Referrers identify companies with at least 20 employees and three years of operating history, connect decision-makers with Mode, and earn at least $50,000 or 6% of customer sign-up revenue for successful referrals. No full-time commitment or exclusivity is required, and participants may pursue additional sales involvement, commissions, and equity opportunities.

Leads the internal AI portfolio, identifying and prioritizing use cases, building prototypes, preparing data and knowledge for AI, managing partners and budgets, driving adoption, and establishing governance. Partners with executives, IT, Legal, and business teams to deliver measurable outcomes while ensuring security, compliance, and responsible AI practices.

Designs, builds, deploys, and monitors enterprise AI and machine learning solutions using Microsoft Azure and Google Vertex AI. Develops intelligent automation and predictive analytics models, conducts performance experiments, integrates AI capabilities with existing systems, and collaborates with business stakeholders and technical teams. Supports the governance and development of an AI Center of Excellence while translating business requirements into scalable technical solutions.

Owns enterprise data architecture, data quality, lineage, governance, and integration across clinical, operational, and financial systems. Develops MAHEC’s AI strategy, governance framework, vendor evaluation processes, and adoption roadmap. Leads responsible AI evaluation with emphasis on bias, privacy, patient safety, data agreements, regulatory requirements, and lifecycle accountability. Partners with organizational leaders, vendors, and governance committees to translate policy into secure, auditable architecture and operational practice.

Develop and maintain cloud software supporting cyber intelligence and AI/ML capabilities on AWS and large-scale compute clusters. Responsibilities include requirements analysis, Java development, cloud integration, distributed systems, RESTful services, testing, validation, troubleshooting, and sustainment. The role also involves Kubernetes, infrastructure as code, CI/CD, Linux scripting, Hadoop-based big-data technologies, microservices, LLMs, RAG, and security-conscious cloud engineering.

Design, optimize, and scale high-throughput, low-latency inference infrastructure for frontier LLM and generative AI models. Responsibilities include load scheduling, balancing, distributed systems, platform scalability, high availability, and rapid enablement of new AI capabilities. The role collaborates with internal and external partners to deliver production-grade Azure AI inference services.

Build pipeline for Via’s AI Labs through account research, buying committee mapping, account planning, and multichannel outreach. Qualify leads, facilitate meetings, provide demos, and support deal closure. Maintain CRM and marketing automation data, analyze campaign performance, and collaborate with commercial teams on sales and regional growth strategies.

Supports AI, analytics, and change leadership consulting practices through project coordination, research, practice operations, client-material preparation, and business development. The role emphasizes outbound prospecting, LinkedIn and email campaigns, CRM and pipeline maintenance, lead qualification, prospect engagement, and scheduling introductory conversations. It also contributes to thought leadership, events, webinars, marketing initiatives, and internal process improvements.

Design, develop, test, and productize AI/ML algorithms. Responsibilities include selecting and analyzing data, training and improving models, evaluating system performance, integrating scalable solutions, validating data and model quality, and identifying distribution issues. The role requires software engineering expertise, machine learning deployment experience, and oversight of iterative verification and validation processes.

Lead software engineering for a digital marketing platform, defining technical vision and multi-year product roadmaps. Manage and mentor a team, oversee scalable architectures and API integrations, conduct code reviews, and collaborate with product, design, data, and technology teams. The role focuses on AWS-based systems, AI-driven automation, data unification, agile delivery, and building high-quality software solutions.

Designs and publishes networking architectures for large-scale GPU clusters and AI infrastructure. Responsibilities include InfiniBand and RoCEv2 fabrics, multi-tenant isolation, Linux and Kubernetes networking, KubeVirt, hybrid connectivity, research, automation, benchmarking, proofs of concept, customer engagements, partner collaboration, technical writing, and conference presentations.

Develops, evaluates, and deploys AI foundation and generative models for weather, hydrology, and earth system applications. The researcher will train and optimize deep learning models on large GPU-based HPC systems, analyze atmospheric and hydrological datasets, benchmark model performance, and collaborate with scientists and public and private sector stakeholders. Responsibilities also include scientific coding, technical analysis, publications, reports, and documentation.

Designs, develops, tests, evaluates, and deploys AI/ML models across generative AI, classification, prediction, computer vision, NLP, and reinforcement learning. Responsibilities include data preprocessing, experimentation, cloud deployment, ETL pipeline development, model validation, performance benchmarking, documentation, technical presentations, staff education, and serving as an AI/ML subject matter expert.

PhD interns analyze and improve machine learning algorithms, design experiments, prepare and evaluate large-scale datasets, and prototype scalable AI systems. They apply research techniques to product scenarios involving search, language models, recommender systems, geospatial intelligence, or personalization. Interns collaborate with Microsoft teams, interpret evaluation metrics, support feature development, and help productionize applied science solutions.

Leads enterprise architecture across global manufacturing, quality, and product development technology landscapes. Manages and mentors 5–8 technical platform owners and solution architects, establishes strategic roadmaps and governance, evaluates greenfield and brownfield investments, and translates business needs into scalable technical designs. The role covers digital manufacturing, MES, LIMS, SCADA, ERP integration, Industry 4.0, vendor strategy, risk mitigation, executive communication, and GxP compliance across pharmaceutical, biologics, sterile fill-finish, and medical device operations.

Owns the end-to-end development and delivery of AI-powered products for enterprise clients. Responsibilities include defining problems and success metrics, shaping requirements and PRDs, managing client relationships, coordinating engineers and designers, driving execution, supporting adoption, and identifying expansion opportunities. The role combines product management, client engagement, delivery leadership, and hands-on operational ownership in a fast-paced applied AI environment.

Lead the quality practice for an AI voice interface product by developing automated tests across desktop, mobile, backend services, and integrations. Establish continuous regression testing and CI/CD release gates, investigate real-world voice workflows and failures, prioritize risk-based testing, and provide release recommendations. Partner cross-functionally throughout development, improve coverage from incidents and defects, and coach QA teammates while remaining hands-on.