Top Artificial Intelligence Jobs

Lead enterprise customer engagements to drive cloud and AI adoption across Azure, data, analytics, and application innovation. Build pipeline, orchestrate partners, qualify and close high-value opportunities, coach teams on AI-enabled sales strategies, and ensure consumption and renewal outcomes through frequent customer and partner interactions in the Greater Los Angeles area.

Coordinate MAI HR team operations and programs: manage calendars, onboarding, events, and communications; track deliverables and risks; create processes, reports, and documentation to improve efficiency and support HR initiatives.

Lead partner-focused AI architecture and enablement on Microsoft Cloud. Provide hands-on guidance, prototypes, and best practices for Azure ML/AI, agentic and Copilot solutions, governance, security, cost optimization, and scalable, outcome-based partner offerings to accelerate production-grade AI adoption.

Design, build, test, and maintain agentic AI applications, automations, and integrations. Implement AI security controls and monitoring, validate agent behavior, troubleshoot and optimize systems, and collaborate with stakeholders to translate requirements into secure, scalable AI-enabled solutions using integrations and cloud/iPaaS platforms.

Lead cross-organization technical programs for Azure Specialized Compute, supporting customer cloud migrations and high-performance/GPU workloads. Drive end-to-end initiatives, mentor junior team members, and coordinate with product and engineering teams to deliver scalable compute services.

Build domain data products and pipelines for AI consumption: create retrieval-ready, semantically annotated, contract-governed assets; deploy agents for metadata, entity resolution and classification; ensure data quality, lineage, certification, and reuse; collaborate with domain stewards, business teams and IT to model, store and serve structured and unstructured data for retrieval and modeling.

Research and develop ML models and simulations on large datasets, collaborate with data engineering to ingest and preprocess data, apply statistical analyses and LLM/RAG techniques, visualize and communicate insights, and support product/process improvements through stakeholder engagement.

Lead AI Engineer to architect, develop, deploy, and support enterprise-scale agentic AI solutions using LLMs, RAG, model orchestration and secure APIs. Ensures security, governance, and compliance, collaborates cross-functionally, evaluates emerging technologies, contributes to AI roadmap, and mentors junior engineers to drive automation, efficiency, and improved customer experience.

Lead organizational adoption and scaling of AI across the company by building capability, defining standards and guardrails, and designing AI-enabled workflows (copilots, agents, human-in-the-loop). Partner cross-functionally to prioritize use cases, operationalize solutions, integrate with data platforms, measure adoption and impact, and transition experiments into durable business processes.

Build, deploy, and operate LLM-powered agents: design tool use and orchestration, monitor production health, evaluate with test suites, implement retrieval/RAG, build integrations and scalable AWS infrastructure with Terraform and CI/CD, enforce guardrails and observability, document architectures, and expand agent adoption across the organization.

Lead development and execution of AI-driven solutions by designing sophisticated prompts and agents, integrating scalable models, creating test plans, and performing QA, volume, and bias testing. Translate business needs into actionable AI use cases, collaborate with delivery and business teams, and ensure solutions meet compliance, reliability, and ethical standards.

Senior presales AI architect who designs and deploys production-ready AI solutions for contact center and CX use cases. Responsibilities include RAG and knowledge architecture design, contextual AI and orchestration, evaluation and observability frameworks, partner enablement and labs, technical escalation, demos and co-selling, integrations via REST APIs and MCP/A2A, and translating technical capabilities into business value while driving partner ecosystem scalability and product feedback.

Lead end-to-end design, development, and delivery of enterprise Generative AI and ML solutions. Architect scalable LLM, RAG, and agent-based systems, drive technical decisions, implement production-ready MLOps/CI-CD, and mentor engineers. Collaborate with product and business stakeholders, ensure monitoring, security, and operational support, and evaluate emerging AI technologies to improve delivery.

Provide technical leadership to design, build, and deploy GenAI and agentic AI solutions. Architect enterprise-grade LLM, RAG, and agentic workflows, guide engineering teams, ensure secure AI practices, and deliver high-impact, complex AI initiatives across the organization.

Develop and deploy AI/ML tools for structural engineering: prepare and analyze datasets, apply Bayesian/MCMC inference, perform FEA-driven data preparation, use regression/clustering and constrained multi-objective optimization, and create visualizations to communicate results.

Design and implement backend orchestration for agent workflows and runtime in microservices. Build scalable LLM-powered systems (RAG, tool-use, multi-step reasoning), integrate LLM/TTS/ASR, develop agents with frameworks like LangChain/Dify/OpenAI SDK, optimize prompts, run integration tests, and collaborate with research and product teams to deploy agentic AI across verticals.

Lead design and delivery of end-to-end technical solutions. Develop, document, and maintain applications focusing on efficiency, scalability, reliability, and security. Act as SME across lifecycle, drive automation for build/test/release, perform code reviews, mentor team members, and collaborate with architects and stakeholders to align solutions with enterprise standards.

Design, build, and operationalize scalable AI/ML solutions (including Deep Learning, Generative AI, and LLMs); develop models, MLOps/LLMOps pipelines, and data infrastructure; apply distributed and high-performance computing; conduct R&D and collaborate with cross-functional teams to deploy production-ready systems and demonstrate business value.

Design, build, and operationalize AI/ML solutions across the full lifecycle: assess data and needs, select/customize models (Deep Learning, Generative AI, LLMs), create scalable data and MLOps pipelines, deploy to cloud/HPC/edge, ensure monitoring and reliability, and collaborate with technical and business stakeholders to deliver production-ready outcomes.

Own and expand a portfolio of strategic AI-native accounts by driving ARR growth, prospecting named AI labs, leading complex multi-stakeholder enterprise sales cycles, running POCs with Solutions Consultants, and coordinating cross-functional teams to deliver technical and commercial value.

Evaluate a personalization feature for Gemini by designing multi-turn prompts using personal data, comparing side-by-side model responses, assessing grounding, integration, and helpfulness, writing clear rationales, extracting debug info, and maintaining strict data hygiene.

Senior Quant in the AI/ML Validation CoE provides model validation and benchmarking for AI/ML and Generative/Agentic AI across bank functions. Responsibilities include independent testing, R&D of AI methods, developing technical guidance, training curriculum, communicating validation outcomes, and advising on model risk mitigation, implementation, monitoring, and remediation.

Lead architecture and delivery of enterprise Salesforce Data Cloud and agentic AI solutions for life sciences clients. Design data models, identity resolution, ingestion, segmentation, CRM integrations, Agentforce/Voice AI workflows, and governance. Drive pre-sale solutioning, design reviews, mentor architects, and contribute reusable patterns and accelerators across programs.

Support development of an AI Tokenomics forecasting model: evaluate model architecture trends, simulate training and inference efficiency across architectures, hardware, KPIs and workloads, diligence companies using model tokens, and forecast economic impacts for investors, corporates, and policymakers.

Design and deliver enterprise-grade AI architectures across generative, agentic, and classical ML. Define NFRs, lead stakeholder workshops, select technologies, and architect agent registries, AI gateways, memory services, RAG pipelines, model adaptation, inference serving, security, FinOps, and observability. Produce architecture artifacts, build prototypes, and mentor cross-functional teams.