Sr IT Architect

Honeywell Technologies; Maharashtra; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Honeywell is seeking a senior AI/enterprise architect to lead cloud-native AI architectures across Azure and Databricks. You will define standards, design GenAI/agentic AI solutions, and guide governance, security, and scalable deployment across the enterprise.

The role partners with AI engineers, data teams, platform teams, and business stakeholders to translate complex problems into reusable AI architectures and reference patterns. Strong experience in GenAI and MLOps is essential.

Qualifications

• 10+ years of IT experience with 4+ years in enterprise/ AI architecture roles.
• Strong hands-on experience with Microsoft Azure cloud architecture.
• Deep expertise in AI/ML platforms, especially Azure Databricks.
• Proven experience designing GenAI solutions (RAG, embeddings, fine-tuning).
• Solid understanding of agentic AI concepts (task agents, orchestration).
• Experience with distributed systems, APIs, and enterprise integration patterns.
• Experience defining reference architectures and technology standards.

Responsibilities

• Define enterprise AI architecture & strategy for GenAI and Azure platforms.
• Create and maintain architecture standards for AI/ML across Azure and Databricks.
• Own reference architectures for GenAI, agentic AI, and AI platform services.
• Design end-to-end AI solutions: data pipelines, training, evaluation, deployment.
• Develop multi-LLM strategies with abstraction layers and fallback patterns.
• Define governance, security, and compliance models for enterprise AI use.
• Lead MLOps / LLMOps standards: CI/CD, versioning, monitoring.
• Architect Azure Databricks pipelines for ML training and inference.

Skills

Azure cloud

AI architecture

Databricks

GenAI / LLMs

MLOps

Security & governance

Tools

APIs

RBAC / IAM

Job description

Job Description

This role leads architecture for cloud-native AI applications leveraging Microsoft Azure, Azure Databricks, LLMOps/MLOps, and enterprise system integrations, ensuring solutions meet standards for security, scalability, governance, and business value.

Job Description

This role leads architecture for cloud-native AI applications leveraging Microsoft Azure, Azure Databricks, LLMOps/MLOps, and enterprise system integrations, ensuring solutions meet standards for security, scalability, governance, and business value.

The architect partners closely with AI engineers, data teams, platform teams, product owners, and business stakeholders to translate complex business problems into referenceable, reusable AI architectures.

Responsibilities

Key Responsibilities

• Enterprise AI Architecture & Strategy
• Define and maintain enterprise-wide architecture standards for AI/ML, GenAI, and Agentic AI platforms across Azure and Databricks.
• Own reference architectures for:
• GenAI applications (RAG, fine-tuning, prompt engineering)
• Agentic AI systems (multi-agent orchestration, agent-to-agent communication)
• AI platform services (model serving, vector search, AI gateways).
• Guide long-term AI platform roadmaps aligned to business strategy and cloud architecture principles.
• AI / GenAI / Agentic AI Solution Design
• Architect end-to-end AI solutions including:
• Data ingestion and feature pipelines
• Model training, evaluation, and deployment
• Retrieval-Augmented Generation (RAG) using vector databases
• Agent frameworks for task orchestration and enterprise workflows.
• Design multi-LLM strategies (Azure OpenAI, open-source, and commercial LLMs) with abstraction layers and fallback patterns.
• Define agent registry, agent orchestration, and governance models for enterprise-scale usage.
• Azure & Databricks Platform Architecture
• Lead architecture for Azure-native AI stacks
• Architect Azure Databricks for: ML training and inference, LLM fine-tuning and evaluation, Vector search and embedding pipelines, MLflow-based lifecycle management.
• Define cost-optimized, secure, and scalable cloud reference patterns.
• Enterprise Integration & Interoperability
• Define integration patterns between AI platforms and: ERP, CRM, PLM, HCM systems. APIs, event-driven architectures, and messaging platforms.
• Architect AI Gateway and API management patterns for GenAI and agent access.
• Governance, Security & Compliance
• Establish AI/GenAI governance frameworks covering:
• Data privacy
• Model risk management
• Responsible AI principles
• Auditability and traceability.
• Ensure architectures integrate with enterprise IAM, RBAC, and SSO.
• Define guardrails for safe LLM usage, prompt leakage prevention, and data isolation.
• MLOps / LLMOps / AgentOps
• Define standards and patterns for:
• CI/CD for AI and GenAI workloads
• Model versioning, evaluation, and drift monitoring
• LLMOps and agent lifecycle management.

Qualifications

Required Qualifications

• 10+ years of IT experience with 4+ years in enterprise architecture or AI architecture roles.
• Strong hands‑on experience with Microsoft Azure cloud architecture.
• Deep expertise in AI/ML platforms, especially Azure Databricks.
• Proven experience designing GenAI solutions (RAG, embeddings, LLM fine‑tuning).
• Strong understanding of agentic AI concepts (task agents, orchestration, memory, feedback loops).
• Solid background in distributed systems, APIs, and enterprise integration patterns.
• Experience defining reference architectures and technology standards.

About Us

Honeywell helps organizations solve the world's most complex challenges in automation, the future of aviation and energy transition. As a trusted partner, we provide actionable solutions and innovation through our Aerospace Technologies, Building Automation, Energy and Sustainability Solutions, and Industrial Automation business segments – powered by our Honeywell Forge software – that help make the world smarter, safer and more sustainable.