Manager, AI & Data Engineering

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• We are seeking a senior Lead AI Platforms Engineer with 10-12 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 GCP.The 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: Bachelor’s degree in Computer Science, Data Science, Information Systems, Engineering, or a related field; master’s degree preferred.
• Overall experience: 10-12 years of relevant technology experience in cloud engineering, AI/ML platforms, data platforms, automation, enterprise application development, or platform architecture.
• 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.
• Experience or Exposure to AWS, Microsoft Copilot, Copilot Studio, Dataiku, GitHub Copilot, Cursor, Codex, Claude, or other enterprise AI and coding assistant tools Benefits We are committed to offering competitive benefits programs for all of our employees, and enhancing our programs when necessary. · Make yourself a priority with flexible schedules, parental leave · Drive forward your career through professional development opportunities · Achieve your personal goals with our Employee Assistance Programme Our commitment to you Our greatest assets are the expertise, creativity and passion of our employees. We strive to provide a great place to work that attracts, develops and retains the best talent, promotes employee engagement, fosters teamwork and ultimately drives innovation for the benefit of our customers. We strive to create an environment where you feel that you belong, with diversity and inclusion as the engine to growth and innovation. We develop and deploy best-in-class programs and practices, providing enriching career opportunities, listening to employee feedback and always challenging ourselves to do better. This is The Carrier Way. Join us and make a difference. Apply Now!

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