Manager, AI & Data Engineering

Carrier; India; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

Carrier seeks a Manager, AI & Data Engineering to lead design and operation of enterprise AI/data platforms in Bangalore. You will shape cloud-native AI solutions, drive automation, and govern multi-agent AI initiatives while mentoring a high-impact engineering team.

This role combines platform architecture with hands-on delivery and governance across GCP and AI tooling. Ideal candidates have 10-12 years in cloud engineering, AI platforms, and data architectures, with strong experience in MLOps,

Qualifications

• Experience designing scalable AI/data platforms on cloud
• Proven ability to govern AI/ML workflows
• Strong knowledge of GCP security controls
• Hands-on experience with MLOps/LLMOps
• Leadership of engineering teams in large-scale programs

Responsibilities

• Design and implement scalable AI, data, and automation platforms on GCP with security and governance.
• Build and operationalize cloud-native AI/ML solutions using Vertex AI, BigQuery, Cloud Storage and related services.
• Architect secure API and data integrations with event-driven workflows.
• Design and implement robust automation workflows using Python, TypeScript, APIs and CI/CD.
• Lead development and governance of AI agents, multi-agent workflows and guardrails.
• Evaluate and adopt AI tools and platforms for governance and business value.
• Define security controls across GCP, monitor reliability and cost efficiency.
• Mentor engineers and set reusable engineering patterns for AI platform work.
• Oversee ML lifecycle, model deployment, monitoring, retraining, and production support.
• Bring 10-12 years of tech experience across cloud engineering and data/AI platforms

Skills

Platform engineering

Automation

AI governance

GCP

MLOps

LLMOps

AgentOps

Security

Tools

Vertex AI

BigQuery

Cloud Storage

Cloud Run

Cloud Functions

Pub/Sub

IAM

Job description

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.

About Role

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.

Role 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.

• 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.

• 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.

• 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.

• 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.

• 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,