Top Tech Jobs & Startup Jobs in Boston, MA

Own product direction for an enterprise AI platform, including requirements, roadmap, acceptance criteria, scope, and stage-gate processes. Translate customer and stakeholder needs into prioritized, testable requirements; manage trade-offs among value, velocity, leverage, and technical risk; and partner with engineering on delivery decisions. Recruit design partners, validate customer needs, document decisions, communicate with executives and clients, and maintain disciplined delivery across multiple product teams.

Lead governance, risk, and compliance activities for an enterprise AI platform. Define and mature SOC 2 and ISO 27001 controls, collect evidence, track remediation gaps, prepare audits, and create customer-facing security documentation. Partner with engineering, security, product, auditors, and client stakeholders to embed practical, automated compliance practices covering data handling, retention, access control, privacy, AI governance, and customer assurance.

Designs and secures enterprise AI platform foundations for mission-critical environments. Responsibilities include threat modeling, tenant isolation, identity and authorization architecture, secure CI/CD, compliance-by-design, and protection of systems executing untrusted code, tools, or agent actions. The role partners with client engineering, product, security, and business stakeholders to deliver secure, reliable, and compliant platform capabilities.

Design, build, operate, and support backend services powering an enterprise AI platform. Responsibilities include developing APIs, orchestration, entitlements, tenancy, metering, integrations, data access, and platform services. Own services across architecture, implementation, testing, deployment, monitoring, reliability, and support while applying distributed-systems principles. Collaborate with client product, engineering, data, and platform stakeholders in a remote consulting environment.

Design, build, and operate cloud infrastructure, deployment, observability, and reliability capabilities for an enterprise AI platform. Responsibilities include infrastructure-as-code, Kubernetes, CI/CD, incident response, SLOs, cost optimization, production readiness, automation, and support for hybrid, edge, on-premises, and customer-controlled environments. The role is client-facing, remote, and may involve travel.

Design, build, and operate cloud infrastructure and SRE capabilities for an enterprise AI platform. Responsibilities include infrastructure-as-code, landing zones, networking, Kubernetes, CI/CD, observability, incident response, SLOs, production readiness, automation, cost optimization, and support for hybrid, edge, air-gapped, and customer-controlled environments. The role is remote, client-facing, and requires strong collaboration and reliability ownership.

Build and operate production AI capabilities across enterprise platforms, including agent runtimes, RAG, model routing, tool calling, guardrails, evaluation, monitoring, and cost optimization. Ship reliable, safe, measurable AI features; develop evaluation frameworks and quality controls; monitor production behavior; and collaborate with client-facing product, engineering, platform, backend, and security teams to deliver practical AI solutions.

Build and operate production AI capabilities, including agent runtimes, model routing, RAG, tool calling, guardrails, evaluation, monitoring, and quality controls. Ship reliable, measurable, safe, and cost-aware AI features, improve systems using production evidence, and collaborate with backend, platform, product, security, and client teams in a consulting environment.

Owns the roadmap, product requirements, scope, acceptance criteria, and stage-gate processes for enterprise AI platform capabilities. Partners with customers, executives, engineers, product leaders, and design partners to translate needs into prioritized decisions and delivery plans. Manages trade-offs among customer value, technical risk, platform leverage, and product velocity while validating solutions and maintaining delivery discipline in a client-facing consulting environment.

Designs, builds, operates, and supports backend services powering an enterprise AI platform. Responsibilities include developing APIs, AI gateway and orchestration capabilities, entitlements, tenancy models, data access patterns, integrations, and platform services. The engineer owns services throughout design, implementation, testing, deployment, monitoring, reliability, and support while collaborating with client product, engineering, data, and platform stakeholders.