AI & Data Engineering
carrier
Job Description
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1. Platform Engineering & Architecture
- GCP platform architecture: Lead the design and implementation of scalable AI, data, and automation platforms on Google Cloud Platform, including secure landing zones, environment strategy, IAM, networking, monitoring, deployment patterns, shared services, and enterprise governance controls.
- Cloud-native AI engineering: Build and operationalize cloud-native AI/ML solutions using Vertex AI, BigQuery, Cloud Storage, Cloud Run, Cloud Functions, Pub/Sub, Cloud Logging, Cloud Monitoring, service accounts, APIs, and related managed services.
- Enterprise integration patterns: Architect secure integration patterns across APIs, enterprise data sources, event-driven workflows, databases, data pipelines, model endpoints, agent workflows, and third-party systems while ensuring scalability, maintainability, security, and compliance.
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2. Automation & Agentic AI
- Automation and orchestration: Design and implement robust automation workflows using Python, TypeScript, APIs, serverless services, CI/CD pipelines, event-driven design, infrastructure automation, and cloud-native orchestration patterns.
- Agentic AI and AgentOps: Lead the development and operational governance of AI agents, multi-agent workflows, tool calling, human-in-the-loop controls, agent monitoring, evaluation, safety guardrails, access controls, incident response, and production support processes.
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3. AI Platform Evaluation & Assessment
- AI platform evaluation and adoption: Evaluate enterprise AI platforms and productivity tools such as Microsoft Copilot, Dataiku, coding assistants, GitHub Copilot, Cursor, Claude, Codex, and other emerging AI tools as good-to-have capabilities, validating their architecture fit, governance readiness, security posture, integration model, and business value.
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3. Governance, Security & Performance
- Cloud security and governance: Define and enforce security controls across GCP, including IAM, least privilege access, network security, encryption, secrets management, audit logging, policy controls, data protection, and responsible AI governance standards.
- Production reliability: Establish monitoring, alerting, logging, tracing, incident response, performance tuning, release readiness, operational runbooks, and support practices for AI, data, and cloud platform services.
- FinOps and optimization: Lead usage analytics, budget controls, cost allocation, model and API usage optimization, resource right-sizing, and executive-level reporting to improve cloud and AI platform cost efficiency.
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4. Technical Leadership & Team Enablement
Lead and mentor junior engineers by providing hands-on technical direction, reviewing architecture designs and code, defining reusable engineering patterns, conducting knowledge-sharing sessions, assigning technical tasks, removing blockers, and ensuring consistent delivery quality across AI platform, GCP, automation, MLOps, LLMOps, and AgentOps initiatives.5. MLOps & LLMOps
Lead the operationalization of ML, generative AI, and agentic AI solutions across enterprise platforms. This includes MLOps for model deployment, lifecycle management, monitoring, retraining support, and release governance; LLMOps for prompt/version management, model evaluation, RAG quality, safety controls, usage tracking, and responsible AI oversight; and AgentOps for agent workflow observability, tool usage governance, guardrails, incident management, and production support. Ensure AI platforms are secure, observable, cost-efficient, resilient, and production-ready.
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