Spclst, AI & Data Engineering

• 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.
• 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.
• 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.
• 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.
• 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.
  Required Technical Qualifications
  • Overall experience: 10-12 years of overall technology experience across cloud engineering AI/ML platforms data platforms automation enterprise application development or platform architecture.
  • Mandatory specialized experience: 4-5 years of hands-on experience as an AI Engineer or AI Platforms Engineer with strong exposure to Google Cloud Platform MLOps LLMOps AgentOps and production-grade AI solution delivery.
  • GCP technical depth: Strong experience with Vertex AI BigQuery Cloud Storage Cloud Run Cloud Functions IAM VPC Cloud Logging Cloud Monitoring Pub/Sub APIs service accounts data pipelines and enterprise-grade deployment patterns.
  • AI platform engineering: Strong understanding of generative AI model lifecycle prompt lifecycle RAG embeddings vector search model evaluation responsible AI controls AI governance observability scalability and platform reliability.
  • MLOps LLMOps and AgentOps: Proven experience with model deployment CI/CD for ML and AI workloads prompt and model versioning evaluation pipelines agent monitoring tool orchestration guardrails usage tracking incident response and production support for AI systems.
  • Core engineering: Advanced proficiency in Python TypeScript JavaScript APIs infrastructure automation data ingestion pipelines backend services and integrations with AI/ML and LLM APIs.
  • DevOps and platform operations: Proven experience with GitHub CI/CD pipelines infrastructure-as-code environment management release governance observability operational readiness and production support for enterprise platforms.
  • Technical leadership: Proven ability to lead junior engineers mentor team members review technical designs and code define standards assign technical work remove blockers and drive high-quality delivery.
  • Good-to-have exposure: Working knowledge of AWS services such as SageMaker Bedrock Lambda S3 IAM CloudWatch API Gateway and Step Functions along with Microsoft Copilot Copilot Studio Dataiku GitHub Copilot Cursor Claude Codex and other coding or AI assistants.

• We are seeking a senior AI Platforms Engineer with 7-10 years of overall technology experience including 4-5 years of hands-on experience in Google Cloud Platform AI engineering MLOps LLMOps and AgentOps. This role will lead the design implementation governance and operationalization of enterprise AI platform capabilities on role requires deep technical expertise across AI platform engineering cloud-native architecture generative AI data integration automation DevOps observability security governance and cost optimization. The engineer will define scalable platform patterns mentor junior engineers review solution designs and code establish engineering standards and ensure AI solutions are secure reliable production-ready measurable and aligned with enterprise governance expectations.

• Education: Bachelors degree in Computer Science Data Science Information Systems Engineering or a related field; masters degree preferred.
• Overall experience: 7-10 years of relevant technology experience in cloud engineering AI/ML platforms data platforms automation enterprise application development or platform architecture.
• Specialized experience: 4-5 years of hands-on experience in Google Cloud Platform AI engineering MLOps LLMOps AgentOps and production AI platform delivery.
• Mandatory cloud skills: Strong hands-on experience with Google Cloud Platform including secure architecture cloud-native services identity networking monitoring cost optimization governance and production operations.
• Programming foundation: Strong hands-on experience with Python TypeScript JavaScript APIs automation scripts backend services and integration patterns.
• AI platform fundamentals: Strong understanding of generative AI ML lifecycle prompts embeddings RAG model evaluation responsible AI AI governance usage monitoring and production reliability.
• MLOps LLMOps and AgentOps: Strong understanding of model deployment prompt lifecycle management model and agent evaluation tool orchestration agent monitoring guardrails observability incident management and production support for AI systems.
• Security and governance: Strong understanding of IAM access control data privacy compliance encryption secrets management audit logging responsible AI and cloud governance principles.
• Technical leadership: Proven ability to lead junior resources mentor engineers review code and designs define technical standards assign technical work remove blockers and drive high-quality delivery across multiple initiatives.
• Good-to-have skills: Exposure to AWS Microsoft Copilot Copilot Studio Dataiku GitHub Copilot Cursor Codex Claude or other enterprise AI and coding assistant tools.
  

  Benefits

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