AI Solution Architect

Role: AI Solution ArchitectLoc: 5 days office in HyderabadExp: 12+ yearsEmployment Type: Full TimeJob SummaryWe are seeking a highly experienced AI Architect / GenAI & Agentic Systems Lead to design, develop, and scale enterprise-grade AI platforms and agentic solutions.The ideal candidate will possess deep expertise across Generative AI, Agent Ops, cloud-native engineering, and AI platform strategy, with hands-on proficiency in LLMs, orchestration frameworks, and multi-cloud deployments.This role requires a strong technical leader who can architect and operationalize AI ecosystems aligned with enterprise governance, scalability, and compliance standards.Key Responsibilities- Design and implement end-to-end AI architectures — spanning data ingestion, experimentation, model deployment, observability, and lifecycle management. - Lead the development of Agentic AI systems with advanced features such as Agent Ops, observability, shared memory, A2A communication, and context-sharing via MCP. - Architect and operationalize enterprise AI marketplaces/registries for governed, self-service publishing and consumption of models, datasets, and agents. - Build and manage horizontal AI services at scale — prompt/RAG APIs, vector & feature stores, guardrails, evaluation, and observability frameworks. - Collaborate with data, cloud, and engineering teams to deliver multi-cloud AI platforms using AWS, Azure, and GCP (especially Vertex AI, Agentspace, ADK). - Implement MLOps, AIOps, and Dev SecOps best practices for AI and agent lifecycle deployment, security, and monitoring. - Develop AI blueprints covering retrieval pipelines, tool use, safety, zero-trust access, and multi-tenant isolation. - Drive transition from traditional AI → GenAI → Agentic architectures, supporting LLM fine-tuning, RAG workflows, and autonomous agents. - Define and enforce AI governance, ethical AI, and data compliance frameworks aligned with enterprise policies. - Lead cross-functional teams, ensuring SDLC and Agile process alignment, while mentoring engineers and data scientists. - Partner with leadership to shape AI platform strategy, integration roadmaps, and enterprise-wide adoption frameworks.Required Skills & AbilitiesProgramming & AI Frameworks- Proficiency in Python, Java, C#, or R. - Hands-on experience with Generative AI frameworks: TensorFlow, PyTorch, LangChain, OpenAI Codex, Claude, Gemini, HuggingFace. - Strong understanding of ML/DL paradigms — supervised, unsupervised, and reinforcement learning.Agentic AI & Autonomous Systems- Experience with AgentOps, Agent Observability, and Agent Lifecycle Management. - Knowledge of MCP (Model Context Protocol) for context sharing and A2A (Agent-to-Agent) communication. - Familiarity with Shared Memory architectures and Agentic Workflow Orchestration. - Ability to design and evaluate deterministic vs autonomous agentic architectures.Cloud & Platform Engineering- Deep proficiency in AWS, Azure, and GCP (especially Vertex AI, Agentspace, and ADK). - Expertise in Infrastructure as Code (IaC) using Terraform, GitOps, Kubernetes. - Strong grounding in SRE practices, including SLOs and error budgets. - Experience integrating AI observability and policy enforcement gateways.MLOps, AIOps & DevSecOps- Practical experience implementing CI/CD pipelines, model monitoring, governance, and versioning. - Skilled in AIOps for automated performance monitoring and DevSecOps for secure AI lifecycle management.AI Architecture & Enterprise Integration- Proven experience architecting AI Platforms, registries, and shared services with lifecycle metadata and chargeback models. - Strong understanding of data mesh, policy-driven AI gateways, and multi-region resiliency. - Knowledge of safety pipelines, evaluation harnesses, and guardrail frameworks for LLMs and agents.Ethical & Responsible AI- Deep understanding of data governance, privacy, and ethical AI principles. - Experience implementing responsible AI frameworks ensuring transparency and compliance.Leadership & Collaboration- Strong leadership, mentoring, and stakeholder engagement abilities. - Excellent communication skills with cross-functional and executive teams. - Proven ability to work in agile environments aligned with SDLC standards.