Top AI & Machine Learning Jobs

Build end-to-end product features across web, APIs, and backend. Develop backend services in Python/TypeScript and frontend in React/TypeScript. Design APIs and data models, integrate and manage AI coding agents and model integrations, maintain clean, tested, and maintainable code for enterprise developer-facing products.

Deploy and maintain AI enablement infrastructure (MCP servers, AI agents, AI features in SaaS), manage service accounts and API keys, automate and containerize services, support enterprise IT systems, document integrations and runbooks, and mentor IT support staff.

The AI Engineer will design, develop, and deploy machine learning models, work on APIs for AI applications, and manage ML pipelines on AWS. Responsibilities include productionizing AI systems and developing scalable solutions in a cloud-native environment.

Lead design and ownership of an end-to-end AI platform for large-scale telecommunications. Define architecture, integration patterns, and standards; guide engineering, ML, and operations teams; ensure scalability, observability, security, and cost efficiency; support vendor evaluations, M&A technical due diligence, and stakeholder communication to deliver measurable business outcomes across multiple European markets.

Lead design and delivery of cloud-native, integration-first AI solutions. Translate business needs into scalable AI architectures (LLMs, RAG), integrate data platforms and APIs, perform architecture reviews and risk assessments, mentor junior staff, and provide technical oversight across client engagements to ensure secure, maintainable, and impactful deployments.

Lead multidisciplinary teams to identify and prioritize operational efficiency opportunities, design AI-enabled and agentic workflows, develop business cases, drive pilots and implementations, and manage change, governance, and adoption to realize measurable business outcomes.

Lead delivery and adoption of an AI-enabled PMO across Professional Services: own program end-to-end, identify high-impact AI use cases and ROI, deliver billable client engagements, enable and train teams, govern AI outputs, and run program cadence including backlog, reporting, and stakeholder alignment.

Build Neptune’s full-stack product and technical foundation as the first engineering hire. Own features from architecture through deployment, develop conversational AI and LLM-powered legal and financial workflows, and create the shared memory and context engine. The role requires direct user engagement, rapid iteration, strong product judgment, and end-to-end ownership in a five-days-per-week New York City office environment.

Own technical architecture and post-sales success for large, multi-brand enterprise accounts using agentic LLM systems. Design integrations, guardrails, observability, and expansion roadmaps across channels (chat, email, voice, SMS). Drive technical adoption, mentor architects, certify partner delivery, run technical reviews, and translate field learnings into product and prescriptive playbooks.

Build and productionize ML systems for de-identification: improve NER, entity resolution, extraction, and document understanding. Drive error analysis, dataset creation, experiments, evaluation, safe rollouts, inference optimization, and active-learning/LLM-assisted pipelines to improve precision, recall, and utility in live pipelines.