Spclst, AI & Data Engineering

Role overview

Platform and AI responsibilities

• Design scalable GCP AI, data, and automation platforms with secure landing zones, IAM, networking, monitoring, deployment patterns, shared services, and governance controls.
• Build cloud-native AI and ML solutions using Vertex AI, BigQuery, Cloud Storage, Cloud Run, Cloud Functions, Pub/Sub, logging, monitoring, APIs, and service accounts.
• Architect secure integrations across APIs, enterprise data sources, event-driven workflows, databases, pipelines, model endpoints, agent workflows, and third-party systems.
• Implement automation and orchestration with Python, TypeScript, APIs, serverless services, CI/CD, event-driven design, and infrastructure automation.
• Lead AI agent and multi-agent workflow development, including tool calling, human-in-the-loop controls, monitoring, evaluation, safety guardrails, access controls, and incident response.

Governance and operations

• Evaluate enterprise AI platforms and productivity tools for architecture fit, governance readiness, security posture, integration model, and business value.
• Define GCP security controls covering IAM, least privilege, network security, encryption, secrets management, audit logging, data protection, and responsible AI.
• Establish monitoring, alerting, logging, tracing, incident response, performance tuning, release readiness, operational runbooks, and support practices.
• Lead usage analytics, budget controls, cost allocation, model and API optimization, resource right-sizing, and executive reporting.
• Operationalize ML, generative AI, and agentic AI solutions through MLOps, LLMOps, and AgentOps practices.

Technical leadership

• Lead and mentor junior engineers through hands-on technical direction, architecture and code reviews, reusable patterns, knowledge sharing, task assignment, and blocker removal.
• Ensure AI platforms and solutions are secure, observable, cost-efficient, resilient, measurable, production-ready, and aligned with enterprise governance expectations.

Required qualifications

• Bachelor’s degree in Computer Science, Data Science, Information Systems, Engineering, or a related field.
• The role purpose states 7-10 years of relevant technology experience; the technical qualifications section states 10-12 years of overall technology experience.
• 4-5 years of hands-on experience in GCP, AI engineering, MLOps, LLMOps, AgentOps, and production AI platform delivery.
• Strong hands-on experience with GCP architecture, cloud-native services, identity, networking, monitoring, cost optimization, governance, and production operations.
• Strong experience with Python, TypeScript, JavaScript, APIs, automation scripts, backend services, and integration patterns.
• Strong understanding of generative AI, ML lifecycle, prompts, embeddings, RAG, model evaluation, responsible AI, governance, monitoring, and reliability.
• Experience with model deployment, CI/CD for AI workloads, versioning, evaluation pipelines, agent monitoring, orchestration, guardrails, incident management, and production support.
• Ability to lead junior engineers, mentor team members, review designs and code, define standards, assign technical work, and remove blockers.

Preferred qualifications

• A master’s degree in a related field is preferred.
• Exposure to AWS services such as SageMaker, Bedrock, Lambda, S3, IAM, CloudWatch, API Gateway, and Step Functions is preferred.
• Exposure to Microsoft Copilot, Copilot Studio, Dataiku, GitHub Copilot, Cursor, Claude, Codex, and other AI or coding assistants is preferred.

Benefits

• Carrier offers a competitive total rewards package that may include benefits and well-being programs, varying by role and location.