Top Artificial Intelligence Jobs in Pittsburgh, PA

Manage CertiK's LinkedIn corporate page and employee account matrix, produce AI-assisted content, analyze engagement metrics, and monitor LinkedIn trends and cross-platform activity to grow brand influence and optimize content strategies.

Lead PolyAI's revenue enablement function: build and manage an enablement team, design onboarding and ongoing training, embed Force Management methodologies (CoM, MEDDPPICC), scale AI-powered enablement tools (Claude Code Deal Advisor, LetterAI), partner cross-functionally, reduce sales cycle, improve ramp and conversion through content, certifications, and adoption tracking.

Sell Mortgage Cadence MCP Essentials and Enterprise to banks, credit unions, IMBs and mortgage lenders. Own full sales cycle, build pipeline, lead discovery, run demos/POCs, design tailored solutions with internal teams, manage complex enterprise sales, represent company at industry events, and maintain market insights and accurate pipeline forecasting.

Design and implement backend features in Go, build data pipelines and REST APIs, work with PostgreSQL and AWS, contribute to scalable system design, and optionally develop React.js user-facing features while collaborating in a fast-paced AI-focused startup.

Manage LiteLLM's top accounts as the technical lead, regularly communicate with developer customers, resolve issues and feature requests, prevent churn, drive product adoption and surface roadmap opportunities to product teams.

Provide in-house legal support across corporate, commercial, technology, privacy, data security, employment, and compliance matters. Draft, negotiate, and manage software/SaaS/AI and other commercial agreements; maintain SEC filings; support regulatory and cross-border corporate governance; develop legal templates and scalable contract processes; coordinate with outside counsel on disputes and handle special projects for leadership.

Build and ship core product surfaces and internal tooling for an AI-focused enterprise platform. Implement data pipelines, deploy with customers, collect feedback, and iterate quickly while working directly with the founder on architecture and execution.

Design, deploy, and maintain HPC and MLOps infrastructure across cloud and on-prem clusters. Manage schedulers (Slurm/PBS), optimize MPI/CUDA stacks, storage and networking, automate deployments with Python/Bash, instrument systems, and enable AI training/inference pipelines while collaborating with product and support teams.

Design and implement realistic cloud infrastructure reinforcement-learning environments to evaluate AI models. Create reproducible scenarios covering distributed systems, networking, IAM, queues, durable storage, observability, rolling deployments, and disaster recovery. Build deterministic validation tests, golden reference solutions, and defective variants; document architectures and collaborate with technical leads. Apply DevOps and infrastructure automation for scalable, secure solutions.

The Head of Engineering will drive technical direction, develop product architecture, and eventually lead the engineering team while coding and building AI systems.