Top Tech Jobs & Startup Jobs in Boston, MA

Own technical transformation for 1-2 enterprise clients as a field CTO: set technical direction, lead cross-functional teams, advise C-suite, and build reusable Tribe IP (architecture patterns, evaluation frameworks, deployment playbooks). Hire and develop teams, translate field lessons into practice, and ensure production-grade AI/ML systems that scale and deliver business outcomes.

Lead product marketing for Tribe's enterprise AI services: develop positioning, launch repeatable offerings, build partner marketing and enablement with Anthropic/OpenAI/Google, and create customer evidence and content to drive go-to-market and field adoption.

Lead design of AI agent experiences—conversation and voice flows, multi-turn dialogs, escalation and error handling. Create user journeys, information architecture, wireframes, and clickable prototypes. Co-facilitate discovery workshops, ground decisions in user research, contribute to design systems, and ensure scalable, production-ready UX patterns for enterprise AI deployments.

Embed with enterprise clients to discover user needs and build AI-powered full-stack features (React/Next.js frontend, Python/Node backend). Rapidly prototype, integrate LLM APIs, harden for production, iterate with users, and contribute to internal libraries and standards.

Deploy, operate, and debug AI systems inside client infrastructure. Own production readiness, networking, observability, cost controls, IaC (Terraform/Pulumi), CI/CD and stakeholder management with client IT/DevOps to ensure scalable, governable AI deployments.

Lead technical discovery and architect end-to-end GenAI and ML systems. Build rapid prototypes, define production-grade architectures (security, observability, deployment), create reference architectures, advise stakeholders, partner with sales and delivery teams, and collaborate with OpenAI/Anthropic to validate approaches and unblock technical constraints.

Lead client-facing AI engagements: design end-to-end AI/ML architectures, drive technical discovery, enable engineers, translate technical choices to business outcomes, and create reusable deployment patterns.

Lead client-embedded product strategy and delivery for enterprise AI solutions (80% onsite). Drive solution discovery, define product vision, prioritize backlogs, prototype, and manage end-to-end implementation. Translate client work into scalable internal products, share best practices, and influence executive stakeholders to ensure value realization.

Embed with client engineering teams to introduce and operationalize agentic coding workflows. Run discovery, redesign SDLC processes, implement agent-friendly codebase changes, upskill engineers, and document repeatable playbooks to scale the practice across enterprises.

Lead client-facing AI engineering engagements: build and deploy production LLM systems using RAG, vector DBs, and agent frameworks; handle cloud deployments, CI/CD, and reliability; debug enterprise issues (auth, networking, data); embed with client teams and document solutions for reuse.