Top Artificial Intelligence Jobs

Leads Microsoft's Cloud & AI Solution Engineering team, coaching technical talent and driving customer adoption of Azure, AI, data, and application platforms. Develops secure, scalable cloud strategies, influences executive technology decisions, advances technical sales pipelines, removes customer blockers, and partners with sales, customer success, engineering, and partners to deliver measurable transformation outcomes.

Develop and maintain production-ready full-stack software using Java, Spring Boot, React.js, AWS, microservices, and DevOps practices. Design scalable, reliable architectures; write and review code; test and operate products; analyze failures; and improve software reliability. Build generative AI applications using LLMs, RAG, vector databases, semantic search, Azure OpenAI, OpenAI APIs, LangChain, LangGraph, MCP, and AI agents while following security, compliance, governance, Agile, and SRE standards.

Design and operate AI-powered multi-agent systems for incident triage, root-cause analysis, and self-service debugging. Build knowledge bases, ingestion and retrieval pipelines, AWS infrastructure, evaluation frameworks, guardrails, and operational documentation. Partner with platform, operations, software, and domain teams to convert production incidents into reliable AI tools and deploy them into audited production environments.

Develop and improve AI agents and machine learning models that identify financial opportunities in healthcare contracts, supply chain, pharmacy, and managed care data. Analyze complex datasets, automate manual workflows, build information extraction and agentic capabilities, and establish evaluation frameworks. Collaborate with engineering, product, value engineers, and healthcare subject-matter experts to translate research into scalable solutions with measurable financial impact.

Build and manage digital sales rooms in Aligned, create buyer-ready concept decks in DIGIDECK, and use Claude with Zoom transcripts to produce accurate room content, action plans, and follow-up materials. Maintain Salesforce deal records, develop reusable templates and prompts, review rooms with sales representatives, provide constructive feedback, and identify recurring objections or content gaps for sales leadership.

Develops, evaluates, deploys, and maintains applied AI and machine learning solutions for national security customers. Responsibilities include prototyping chat systems and domain-specific copilots, tuning prompts and retrieval, building Python APIs and user interfaces, evaluating model performance and safety, and translating mission needs into business-facing AI capabilities. The engineer collaborates with data engineers, data scientists, architects, MLOps engineers, and product stakeholders.

Lead an agile engineering pod developing secure, scalable AI-powered production features. Responsibilities include directing AI coding agents, establishing architectural guardrails, transforming prototypes into reliable software, managing CI/CD and evaluation infrastructure, and leading hiring, mentoring, performance coaching, and career development. The role requires technical architecture expertise across C#/.NET, front-end frameworks, databases, APIs, ETL, and ethical AI practices.

Lead pod-wide quality strategy and global QA teams while personally developing test automation. Evaluate AI-driven testing tools and methodologies for LLM, computer vision, speech recognition, and AI agents. Oversee regression testing, release management, cloud and virtualization test environments, quality metrics, coverage analysis, and production incident root-cause analysis. Coach cross-functional engineering teams and communicate technical risks to improve software reliability.

Lead an agile engineering pod developing secure, scalable AI-powered production features. Direct AI coding-agent workflows, establish architecture and quality guardrails, modernize prototypes, and oversee error handling, testing, security, and observability. Manage hiring, onboarding, mentoring, career development, and performance coaching. Build shared repositories, CI/CD pipelines, evaluation datasets, and data-access standards while aligning engineering delivery with product strategy, risk management, and enterprise standards.

Lead pod-wide software quality strategy and global QA teams while personally developing test automation. Evaluate AI-driven testing tools and test embedded AI components, including LLMs, computer vision, and speech recognition. Manage regression and release strategies, cloud or virtualization test environments, quality metrics, root cause analysis, and cross-functional coaching. The role requires hands-on automation with Playwright, Python, C#, or Java and strong AI testing expertise.

AI Product Management Intern will identify and evaluate AI opportunities across KEEN, research emerging capabilities, define use cases, support prototypes and pilots, document findings, and create playbooks and training materials. The role involves workflow analysis, cross-functional collaboration, responsible AI practices, and recommendations regarding privacy, security, accuracy, bias, data quality, and human oversight. This is a 10-week, 40-hour-per-week onsite internship in Portland, Oregon.

Design, build, and deploy enterprise-scale generative and agentic AI applications using LLMs, RAG, vector search, and multi-agent architectures. Develop secure Python APIs, AI-powered web applications, cloud-native platforms, and production deployment infrastructure. Implement observability, LLMOps, governance, and responsible AI practices while integrating enterprise systems. Collaborate with product, engineering, architecture, and business stakeholders to deliver reliable AI solutions.

Leads Cash Depot’s company-wide AI and automation program, identifying business opportunities, evaluating tools and vendors, managing pilots, guiding prototypes, and driving adoption. Partners with engineering, security, compliance, HR, and business leaders to establish governance, measure outcomes, manage risks, and report progress to the CTO. The role combines process analysis, technical judgment, stakeholder management, change leadership, and hands-on execution.

Lead the architecture of PointClickCare’s AI Platform, including agentic runtime, MCP integration, model gateway, evaluation, and observability services. Guide AI engineers through technical decisions, design reviews, and mentorship. Partner with product teams to translate roadmaps into architecture requirements, document decisions, establish security and responsible AI standards, and provide reusable patterns that enable engineering teams to build scalable AI agents and solutions.

Owns full-cycle recruiting for engineering and research roles, partnering with hiring managers and founders to define candidate profiles, source passive talent, screen candidates, manage agencies, close offers, and build technical talent pipelines. The role focuses on hiring engineers and specialists in AI evaluations, security, research, and forward-deployed engineering while using funnel data, talent mapping, events, and networks to improve recruiting outcomes.

Build and ship full-stack AI visual creation products, including music videos, generative cover art, and animated video covers. Develop preference data pipelines, integrate and evaluate image and video generation models, and create agentic AI director workflows. Collaborate with designers and product managers to shape product scope and improve visual quality.

Leads the strategy, architecture, implementation, operations, and continuous improvement of enterprise business platforms across Finance, People, Legal, and Procurement. Develops a multi-year technology vision, drives standardization and AI-enabled automation, oversees platform reliability and compliance, manages vendors and investments, and builds a lean global team. Partners with executive business leaders to deliver scalable, integrated employee experiences and measurable operational improvements.

Leads People transformation and AI initiatives from strategy through enterprise implementation. Designs future-state workflows, integrates AI, automation, self-service, and enterprise systems, prototypes solutions, establishes technical standards and performance metrics, and develops reusable transformation frameworks. Partners with People and IT leaders to define requirements, evaluate build-versus-buy decisions, align resources, and drive scalable cross-functional adoption.

Leads the strategy, roadmap, backlog, platform decisions, and value measurement framework for AIOps capabilities across application and infrastructure domains. Evaluates LLM and ML tradeoffs, build-versus-buy options, adoption metrics, model risk, and governance requirements. Partners with engineering, operations, risk, audit, and executive stakeholders to prioritize AI-enabled capabilities, demonstrate business value, and discontinue initiatives that lack strategic or measurable impact.

Defines multi-generation memory and data-transport architecture for AI/ML accelerator silicon. Owns HBM, DDR-family, SRAM, controllers, caches, scratchpads, on-die interconnects, DMA engines, and workload-driven performance and power tradeoffs. Leads architecture across silicon generations, vendors, and internal teams, using analytical and simulation models to optimize bandwidth, latency, scalability, and energy per bit.

Design and review scalable AWS architectures for consumer applications, integrate production-grade AI and LLM workflows, and establish secure infrastructure, deployment, monitoring, reliability, and cost controls. Partner with founders and engineers on prototypes, code and infrastructure reviews, load testing, documentation, and handoff. Deliver architecture assessments, implementation plans, infrastructure code, benchmarks, operational runbooks, and knowledge transfer within agreed project scopes.

Leads the technical architecture and strategy for enterprise AI solutions. Designs and deploys production-grade RAG systems, agentic workflows, LLM orchestration, and predictive analytics platforms. Builds scalable, secure, observable, multi-tenant AI systems while managing performance and costs. Partners with product, data science, and engineering teams, mentors developers, establishes technical standards, and influences technology roadmaps.

Own and evolve the AI Accountant’s full-stack systems, with emphasis on backend architecture, reliable third-party data ingestion, idempotent processing, background jobs, account synchronization, and production operations. Integrate LLMs and ML into classification and reconciliation workflows with strong validation and failure handling. Design PostgreSQL schemas and ensure transactional correctness while shipping frequently and owning systems end to end.

Leads architecture, design, and hands-on implementation of an AWS-based intelligent call-routing platform using AI, conversational interfaces, and programmable communications. Defines API, microservices, serverless, and event-driven architectures; develops with Node.js, Python, and React; conducts architecture reviews; mentors teams; creates proofs of concept; and advises stakeholders through technical workshops. The role requires deep AWS expertise, contact center platform experience, technical leadership, and 25–30% travel.

Leads executive and stakeholder discovery, business process and value-stream design, and requirements definition for data, AI, and GenAI initiatives. Translates ambiguous business needs into structured requirements, user stories, workflows, data specifications, and success metrics. Supports pilots, UAT, change management, training, and adoption while partnering with technical teams. Builds client relationships, ensures deliverable quality, coaches project members, and uses generative AI tools to accelerate analysis and solution design.