AI Solutions Architect

Job Summary

AI Architect / GenAI & Agentic Systems Lead

Generative AI, AgentOps, cloud-native engineering, and AI platform strategy

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.