Top AI & Machine Learning Jobs

Lead and line-manage a 10–15 person engineering team building AI-native agentic observability platforms. Own roadmap, backlog, delivery, quality, and production releases. Drive agile execution, introduce AI-driven SDLC and LLM tooling, mentor engineers, and support hiring, scaling, and organizational growth while collaborating with product and cross-functional teams.

Design and maintain AI-first product documentation: define content models, taxonomy, and modular content; curate and review AI-generated drafts; build prompts, templates, and agent skills; partner with SMEs and product teams; enforce metadata and retrievability standards; mentor writers; work in docs-as-code using Markdown, VSCode, Git, and agentic tooling.

The AI Engineer designs and deploys AI automation tools, partners with teams for workflow automation, and enhances operational efficiency using generative AI and data-driven decision support.

Lead development of OpenTelemetry ingest for GenAI workloads, validate integrations across AI SDKs and frameworks, align auto-instrumentation with OneAgent, drive OpenTelemetry GenAI semantic conventions, prototype and ship production features, set technical strategy, and mentor engineers.

Design and build evaluation and simulation systems for generative AI agents using Dynatrace observability data. Create large-scale simulation pipelines, define metrics/datasets/judging strategies, build developer CLIs, generate adversarial scenarios, and measure agent tool-use and failure modes. Prototype, run feedback cycles, set technical strategy, and mentor engineers.

Build and maintain core Python agent layers and LLM routing, implement tool-calling infrastructure and guardrails, design validation and prompt-injection defenses, define datasets and metrics for automated agent evaluation, drive architectural decisions, and own testing, debugging, and production reliability for LLM-powered agent systems.

Design, build, and maintain high-performance Go backend services for a multi-tenant AI agent platform. Own authentication, token/session lifecycle, scoped credential granting, and data isolation. Harden the platform against web and AI-specific threats, contribute to backend features, database and infrastructure work, and drive architectural decisions, testing, and debugging.

Drive AI feature development and integration across the product lifecycle. Provide technical advice, validate proposals, analyze tech and market trends, propose implementation plans, improve UX and workflows, and collaborate with CTO and product leadership to ensure emerging technologies are reflected in product strategy.