Senior role focused on AI platform engineering with responsibility for architecture, design, and delivery of enterprise-scale AI solutions. Requires 12-18 years experience in building enterprise web applications and hands-on expertise in Generative AI, ML solutions, MLOps, and agentic AI workflows, preferably using Microsoft technologies. Strong skills in Python, backend architectures, scalable APIs, and AI platform capabilities including vector databases, model serving, and observability. Experience with cloud AI platforms such as AWS Bedrock, Azure OpenAI, and Databricks is required. Responsibilities include leading AI platform architecture, defining standards and guardrails, managing AI governance and cost, mentoring engineers, and partnering with business units to scale AI use cases. Emphasis on responsible AI practices including security, privacy, and compliance. Location: Bengaluru, Karnataka, India.
What you'll do
Understand end-user AI solution requirements and translate to architecture and design
Deliver platforms for effective and secure AI use focusing on cost management, governance, and discoverability
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Requirements
12-18 years of experience building enterprise scale web applications
Hands-on experience building Generative AI solutions, ML solutions, MLOps pipeline, and agentic AI workflows
Strong engineering skills in Python and modern backend architectures
Ability to design scalable APIs, services, integrations, and reusable libraries for AI applications
Deep understanding of LLM application patterns including RAG, prompt orchestration, tool/function calling, structured outputs, agentic workflows, multi-agent orchestration, and human-in-the-loop controls
Experience with AI platform capabilities such as vector databases, feature stores, model serving, evaluation frameworks, observability, tracing, usage metering, and cost optimization
Working knowledge of cloud AI and data platforms such as AWS Bedrock, Azure OpenAI, Azure AI Foundry, Databricks, containerized services, serverless patterns, and cloud-native deployment approaches
Ability to embed responsible AI practices including security, privacy, RBAC, model governance, auditability, compliance controls, restricted-topic handling, and production-readiness reviews
Proven ability to influence senior engineers, architects, product leaders, and business stakeholders through clear technical communication and architecture leadership
Exposure to enterprise scale AI solutionsAccess to global user base and internal/external solutionsOwnership of strategic AI platforms and solutionsWork with top AI talentOpportunity to shape enterprise-wide AI architecture and standardsEmployee well-being focusCollaborative work environmentOpportunities for growth, learning, and career advancementInnovation-driven cultureWork-life balance and flexibilityDiversity and inclusion commitment
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