Manager, AI & Data Engineering

Role :   Manager, AI & Data Engineering Location :  Bangalore Full/ Part-time :   Full time   About Carrier   Carrier Global Corporation, global leader in intelligent climate and energy solutions, is committed to creating innovations that bring comfort, safety and sustainability to life. Through cutting-edge advancements in climate solutions such as temperature control, air quality and transportation, we improve lives, empower critical industries and ensure safe transport of food, life-saving medicines and more. Since inventing modern air conditioning in 1902, we lead with purpose: enhancing the lives we live and the world we share. We continue to lead because of our world-class, inclusive workforce that puts the customer at the centre of everything we do. For more information, visit  corporate.carrier.com  or follow Carrier on social media at @Carrier.

Builds enterprise data and AI capabilities to enable secure, scalable, and high-quality data-driven decisions. Applies AI/ML, automation, and strong governance to drive efficiency and business value.

Responsibilities: 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