Top AI & Machine Learning Jobs

Design, build, and operate agentic AI workflows and MCP servers for an LLM-based network operations assistant. Implement LangGraph orchestration, RAG/memory systems, evaluation frameworks, and production integrations (Kubernetes, CI/CD, containers, FastAPI). Collaborate with domain experts, ensure observability, and drive reliability of non-deterministic AI systems at scale.

Own and operate DevOps/MLOps and GenAI production platforms: manage incidents, SLAs, observability, CI/CD and model deployment (RAG, prompt tuning), maintain data and RPA pipelines, enforce AI governance, mentor engineers, and coordinate stakeholders to improve reliability, AI quality, and delivery velocity.

Lead vision, strategy, and execution for AI-powered support products. Own roadmap, customer discovery, launch, adoption, and continuous optimization. Partner with engineering, data science, UX, and support to deliver generative AI, agent-assist, and autonomous support solutions that improve CSAT, reduce MTTR, increase productivity, and lower operational costs.

Lead strategy, roadmap, and delivery of AI-driven networking products using ML, LLMs, generative and agentic AI. Translate customer needs into product requirements, define KPIs, align cross-functional teams, drive go-to-market and thought leadership, and mentor product managers.

Design and develop RPA and agentic AI solutions: build RPA projects, design AI agents using agentic frameworks and LLMs, implement RAG, vector DBs, semantic search, document intelligence, and integrate APIs with human-in-the-loop governance.

Design, develop, and deploy advanced ML and deep learning models for product and system improvements. Lead research on state-of-the-art techniques, perform feature engineering, model selection, training, hyperparameter tuning, and monitoring. Integrate models into production, ensure scalability and robustness, and provide technical leadership and mentorship. Collaborate with cross-functional teams and present technical strategies to senior stakeholders.

Design and build scalable backend services and APIs for HPE EdgeConnect Orchestrator, integrate AI/LLM-driven workflows and Copilot capabilities, work with analytics and telemetry for real-time insights, and ensure quality through testing, debugging, and performance optimization.

Designs, implements, and deploys scalable AI/ML solutions: selects algorithms, engineers features, trains and tunes models, integrates models into production, monitors performance, mentors team members, leads design reviews, and communicates technical strategy to stakeholders.

Lead AI product strategy and roadmaps, partner with engineering and data science on model development, define training data and performance benchmarks, manage product lifecycle from concept to deployment, and align cross-functional teams on business cases, go-to-market, and financial outcomes.