AI Solutions Architect

Job Summary

We 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, AgentOps, 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 AgentOps, 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 DevSecOps 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 & Abilities

Programming & 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.