Top AI & Machine Learning Jobs

Develop and deploy production-ready full-stack AI applications using Python, Django, React, machine learning, NLP, generative AI, and LLM APIs. Build REST APIs, maintain PostgreSQL schemas, integrate vector databases, and support cloud-native deployments across major cloud platforms. Automate marketplace publishing, CI/CD pipelines, containerization, orchestration, and secure software delivery using Jenkins, Git, Artifactory, Docker, and Kubernetes. Collaborate with DevOps, cloud, and data engineering teams.

Leads the technical direction for Microsoft 365 and AI initiatives, designing and delivering Copilot solutions, declarative agents, AI workflows, and enterprise integrations with Microsoft Graph, Teams, and SharePoint. Defines AI architecture, grounding, retrieval, and agent design practices while mentoring teams and evaluating emerging technologies. Also develops and supports GEMS platform features using C#, .NET, Blazor, REST APIs, and SQL Server, including troubleshooting, code reviews, releases, and documentation.

Supports data science projects by preparing and validating datasets, maintaining metadata, conducting exploratory analysis, developing and validating analytical models, researching data science solutions, and visualizing model insights. Works with team members to provide analysis and recommendations for business decision-making. The role requires a bachelor’s degree in data science, statistics, operations research, computer science, or an equivalent field, and typically targets candidates with 0–2 years of experience.

Leads technical strategy and multi-year roadmaps for production-grade generative AI and LLM platforms. Designs scalable, low-latency, multi-tenant distributed systems; develops RAG, agentic, evaluation, safety, and inference capabilities; and applies prompt engineering, fine-tuning, and model customization. Oversees observability, reliability, security, privacy, capacity, and cost management. Provides technical leadership, mentorship, cross-team influence, rapid prototyping, and architecture guidance across product areas.

Designs and maintains intelligent monitoring and incident-response workflows across cloud and on-premises environments. Builds operational data pipelines, develops predictive models for anomaly detection and outage forecasting, automates remediation runbooks, and integrates alerting with service-management platforms. Collaborates with DevOps, SRE, and security teams to improve reliability, performance, availability, and governance.

Designs and maintains intelligent monitoring, incident response, observability data pipelines, predictive anomaly and outage models, and automated remediation workflows across cloud and on-premises systems. The role integrates alerting with service management platforms and collaborates with DevOps, site reliability, and security teams to improve platform reliability, availability, performance, and operational governance.

Design, build, and deploy prompt-engineered AI agents and copilots for sellers. Create and tune prompts, connect LLMs to GTM systems (Salesforce, Snowflake, Databricks), ship production workflows, run experiments, monitor performance, and drive field adoption while ensuring security, privacy, and responsible AI governance.

Design and implement large-scale, AI-driven SD-WAN and AIOps systems. Develop fault-tolerant, real-time, message/event-driven services in C++/Python/Go. Drive feature design, code and design reviews, testing, debugging, optimization, and end-to-end delivery while collaborating with product teams.

Designs and executes validation strategies for HPE rack-scale HPC and AI systems across hardware, firmware, OS, storage, networking and management interfaces. Troubleshoots integration issues, improves automation and test coverage, coordinates global test execution, supports Linux certification, and drives defects to resolution while mentoring and guiding test teams.

Build an agentic AI harness for a conversational interface using frontier or open-weight LLMs. Develop agentic platforms using AI agents, MCP tools, skills, semantic retrieval, memory, monitoring, and workflow automation. Design scalable APIs and integrations, implement safe and performant solutions, collaborate across Product and QA, participate in design reviews, and support production deployment using modern engineering practices.