Top Artificial Intelligence Jobs

Develop and deploy generative AI and LLM solutions on Google Cloud Vertex AI. Build data pipelines integrating structured and unstructured data, implement prompt engineering and RAG, and connect AI capabilities to dashboards and real-time APIs. Translate business needs into technical solutions, stay current with generative AI advances, and communicate analytical results to technical and non-technical audiences.

Lead applied AI initiatives by co-developing AI agents, embeddings-based solutions, and proprietary language-model applications with business teams. Identify AI use cases, develop rapid proofs of concept, mentor junior team members, create trainings and technical documentation, guide production implementations, troubleshoot integrations, and collaborate with engineering partners to deliver modular solutions.

Lead Engineer to architect, build, and deploy production-grade GenAI and agentic AI systems grounded in knowledge graphs. Responsibilities include neuro-symbolic design, knowledge-graph modeling and databases, agent orchestration, LLM integration, scalable APIs, data pipelines, governance, reliability, and mentoring engineers across enterprise domains.

Design, build, and operate large-scale AI infrastructure for GPU-based training and inference. Responsibilities include distributed scheduling, storage and networking optimization, observability, checkpointing, fault tolerance, security, automation, cost control, and developer tooling. The engineer will support hybrid cloud and on-premise environments, integrate major ML frameworks, plan capacity with research teams, and maintain operational documentation. The role requires extensive infrastructure or HPC experience, strong Python and systems programming skills, and expertise in distributed training and accelerator platforms.

Design, optimize, and deploy machine learning models for mobile, embedded, and accelerator-based edge devices. Apply quantization, pruning, distillation, and hardware-aware optimization to improve latency, energy efficiency, and memory usage. Build cross-platform inference runtimes, hybrid edge-cloud systems, secure model update workflows, privacy-conscious telemetry, and benchmarking suites. Collaborate with hardware, firmware, and product teams while documenting systems and evaluating emerging edge AI technologies.

Build and operate petabyte-scale data infrastructure for AI training, evaluation, and continual improvement. Responsibilities include multimodal data ingestion, cleaning, deduplication, quality assurance, dataset versioning, lineage, high-throughput delivery, labeling workflows, storage optimization, privacy enforcement, observability, and evaluation dataset construction. The role partners with ML teams to improve model quality and training efficiency while maintaining reliable, reproducible pipelines.

Lead frontend architecture and technical direction for Observe by Snowflake’s AI Products team. Build interactive interfaces for streaming, multi-step AI agents; develop Slack integrations, CLI tooling, evaluation pipelines, MCP integrations, and extensible platform capabilities. Partner with product and design on roadmap decisions, mentor engineers, author architecture guidance, and deliver scalable customer-facing web applications for AI-powered observability.

Leads AI modernization for energy, environment, and infrastructure business development, including pursuit and proposal workflows, AI use-case prioritization, agent and prompt evaluation, governance, workflow integration, adoption, training, and continuous improvement. Coordinates business development, technology, delivery, risk, compliance, and learning teams to scale repeatable AI capabilities and measure business impact.

Build and deploy production-quality AI/LLM agents, internal tools, and automated workflows to boost Solutions Architecture productivity; collaborate with SAs and cross-functional teams, harden prototypes into scalable systems, and maintain backend systems and demo assets.

Design, build, deploy, and govern AI agents supporting credit risk, portfolio monitoring, compliance reviews, and risk reporting. Partner with Technology and Data Engineering to create enterprise-grade, auditable solutions using LLMs, agent orchestration, RAG, and MCP. Translate complex risk workflows into scalable applications, maintain deployed agents, evaluate performance, ensure regulatory compliance, mentor junior team members, and contribute to responsible AI governance and broader AI/ML strategy.

Lead enterprise Azure and AI agent platform engagements from assessment through production delivery. Design landing zones, migrations, networking, identity, governance, FinOps, Microsoft Foundry environments, Copilot Studio solutions, and evaluation-driven CI/CD pipelines. Develop infrastructure and agents in code, author client-ready architecture and operational documentation, lead consulting teams, present to executives and governance boards, and enable client teams to operate delivered platforms.

Lead enterprise AI initiatives as a principal engineer and solution architect. Design, prototype, and deploy scalable generative AI, RAG, agentic AI, and predictive solutions. Translate business needs into technical requirements, evaluate technologies and vendors, conduct architecture reviews, and establish AI engineering, trust, safety, monitoring, and compliance standards. Mentor engineers and guide responsible AI practices across customer-facing products and internal operations.

Designs, builds, deploys, and scales enterprise AI solutions across machine learning, generative AI, and agentic systems. Architects reusable AI services and frameworks, guides model selection and operationalization, and establishes standards for scalability, security, observability, governance, and responsible AI. Leads modernization of legacy systems through AI-assisted development, influences enterprise technical direction, makes buy/build/integrate decisions, and mentors AI engineers while partnering with business, product, engineering, security, and data teams.

Deploys and validates complete AI infrastructure software stacks on multi-node GPU systems, then converts implementations into technical documentation, automation, demos, training, and reference architectures. Tests prerelease software, evaluates interoperability and operational resilience, supports partners and field teams, collaborates with engineering and open-source communities, and recommends product improvements based on customer feedback. The role presents solutions through briefings, workshops, webinars, events, and internal training, with some travel required.

Lead end-to-end performance optimization for LLM and VLM inference on NVIDIA GPU systems. Analyze workloads, build performance models, profile systems, optimize CUDA kernels and serving runtimes, and tune batching, caching, quantization, speculative decoding, and parallelism. Establish reproducible benchmarks and regression gates while collaborating across model, framework, networking, and GPU architecture teams. Contribute improvements to open-source inference projects including TensorRT-LLM, vLLM, and SGLang.

Lead and develop Analytics Engineers responsible for shared data foundations, platform capabilities, semantic models, developer tooling, and AI-enabled workflows. Define the team vision, operating model, roadmap, standards, and success measures. Partner cross-functionally to prioritize initiatives, improve adoption, guide architecture, and deliver scalable data assets and automation. Drive responsible AI-assisted development, data reliability, testing, documentation, observability, and cost management while contributing hands-on when needed.

Design, build, and operate secure, production-grade AI applications, workflow automation, APIs, data pipelines, integrations, reusable solution accelerators, and observability tooling. The role partners across Marketing, Sales, PMO, GTM, Delivery, and Operations to reduce manual work and improve efficiency. Responsibilities include event-driven application development, LLM and RAG implementations, database and ETL work, AWS cloud development, API design, security, and automated testing.

Lead strategy and execution for a mobile consumer AI assistant, improving task completion, response quality, adoption, engagement, and retention. Use analytics, experimentation, research, and AI evaluations to prioritize improvements. Partner with engineering, AI/ML, design, safety, and marketing to enhance grounding, latency, reliability, and safety while driving cross-functional initiatives and communicating results to senior leadership.

Designs enterprise learning experiences by combining learning science, UX/UI design, AI prototyping, and client consulting. Responsibilities include creating wireframes and functional prototypes, designing learning journeys across digital and live modalities, managing AI-related risks, analyzing learning metrics, facilitating design sprints, supporting learning events, and contributing to client proposals and executive presentations.

Build and deploy AI-powered tools and workflows for legal operations, including contract review, research, drafting, compliance, and knowledge management. Develop prompt frameworks, integrations, governance controls, quality procedures, and responsible-use policies. Evaluate vendors, assess technical and security risks, train legal teams, document workflows, track adoption and ROI, and collaborate with attorneys, IT, privacy, security, and business stakeholders.

Leads enterprise AI strategy, architecture, and production implementation across corporate functions, contact centers, and GTM products. Designs generative and agentic AI solutions, conversational and voice agents, RAG systems, intelligent workflows, and multi-agent orchestration on Azure and Google Cloud. Establishes AI governance, observability, SecOps, escalation, and operational models while influencing executives, product teams, and engineering organizations. Mentors technical staff and drives measurable AI adoption and business outcomes.

Design and deploy AI/ML solutions, including agentic AI systems, multi-agent LLM applications, enterprise knowledge systems, and predictive models. Partner with stakeholders to solve high-value business problems, establish evaluation frameworks, and implement monitoring, guardrails, explainability, and responsible AI practices. Collaborate with engineering teams on scalable production deployment, advise on AI strategy, and mentor colleagues.

Lead the data strategy and own end-to-end data pipelines for AI training, simulation, and deployment in safety-critical autonomous systems. Build data flywheels, integrate with large-scale GPU/TPU training infrastructure, ensure simulator fidelity, partner with safety and certification teams, mentor engineers, and track KPIs for pipeline health and model readiness.

Industry Expert who reviews and defends advanced AI/ML and systems engineering projects, chairs architecture and executive review panels, hosts occasional technical sessions, and sets standards for instructors. The role requires production-scale engineering experience, live architecture critique, a public technical footprint, strong English, and expertise in agentic systems, LLM evaluation, model serving, cloud infrastructure, distributed systems, observability, RAG, and enterprise deployments.

Supports client assessments and governance for AI, GenAI, cloud, data, and cybersecurity risks. Responsibilities include evaluating model lifecycle controls, responsible AI practices, IAM, encryption, logging, monitoring, data governance, documentation, testing, lineage, and change management. The role applies software and data engineering knowledge to translate technical designs into risk and control implications, collaborates with engineering and risk teams, gathers requirements, manages assigned tasks, and prepares client-ready reports.