AI Solutions Architect; Agentic AI

AI Solutions Architect (Agentic AI)

We believe work should be innately rewarding and a team-building venture. Working with our teammates and clients should be an enjoyable journey where we can learn, grow as professionals, and achieve amazing results. Our core values revolve around this philosophy. We are relentlessly committed to helping our clients achieve their business goals, leapfrog the competition, and become leaders in their industry.

What drives us forward is the culture of creativity combined with a disciplined approach, passion for learning & innovation, and a ‘can-do’ attitude!

We're seeking a strategic and hands-on AI Solutions Architect who can define and lead the architecture of enterprise-scale Agentic AI solutions. The ideal candidate combines deep technical expertise in generative AI, cloud platforms, enterprise architecture, and AI governance with the ability to influence business and technology leaders. You are passionate about turning emerging AI capabilities into secure, scalable, and high-value business outcomes while establishing the standards, guardrails, and architectural patterns that enable multiple teams to successfully deliver AI-powered solutions.

As an AI Solutions Architect (Agentic AI), you will partner with Architecture and Engineering leadership to shape the organization's AI strategy, reference architectures, and technology roadmap. You will be responsible for designing enterprise AI platforms, agentic AI frameworks, RAG architectures, governance standards, security controls, and integration patterns that support scalable and responsible AI adoption. This role requires evaluating emerging technologies, guiding build-versus-buy decisions, establishing engineering best practices, and collaborating across business, security, data, and technology teams to deliver innovative AI solutions that drive measurable business value.

• Shape the AI strategy and roadmap: Align AI capabilities and investments with business priorities, while honestly assessing data, technology, and organizational readiness and sequencing initiatives for the greatest impact.
• Define the architecture for agentic AI: Establish enterprise reference architectures for different agent patterns, including supervisor/worker, peer-to-peer, sequential workflows, and provide clear guidance on when to use each approach.
• Architect the AI knowledge and memory layer: Design how AI solutions manage session context, long-term knowledge, vector stores, knowledge graphs, and audit or episodic memory so information can be retrieved consistently and reliably.
• Establish enterprise RAG patterns: Define standards for retrieval-augmented generation, caching, embeddings, vector search, reranking, data lineage, observability, and evaluation so teams can build solutions consistently and at scale.
• Set standards for AI tools and agent interfaces: Define patterns for APIs, MCP servers, agent tools, and inter-agent communication so capabilities can be securely reused across applications and delivery teams.
• Lead build-versus-buy decisions: Evaluate models, platforms, and vendors based on business value, capability, cost, scalability, flexibility, and long-term risk, including decisions around multi-model strategies, the use of both frontier and smaller language models.
• Help determine where AI belongs: Challenge whether a problem truly needs an agent, should use a deterministic workflow, or should not be automated. Make these decisions based on business value, risk, complexity, and measurable outcomes.
• Establish GenAIOps and engineering standards: Define enterprise practices for AI development, CI/CD, infrastructure, deployment, monitoring, and observability, including metrics such as latency, token consumption, decision traces, hallucination rates, and cost per outcome.
• Architect enterprise AI guardrails: Establish patterns for reducing hallucinations, protecting against prompt injection, safeguarding PII, preventing data loss, detecting bias, and controlling model outputs.
• Design secure agent identity and authorization: Define how agents authenticate and operate with least-privilege access, secure credentials, tenant isolation, row- and column-level permissions, and protection against privilege escalation across chained tool calls.
• Drive responsible AI governance: Apply frameworks such as NIST AI RMF, ISO/IEC 42001, and OWASP LLM Top 10 while partnering with Security, Legal, Data Governance, and Compliance teams on data classification, auditability, residency, and regulatory requirements.
• Architect enterprise integrations: Define scalable integration patterns between AI and enterprise platforms such as identity, collaboration, loyalty, CRM, CDP, ERP, and data Lakehouse.
• Partner across business, product, and technology teams: Work with business leaders, product teams, engineering, security, data, and infrastructure teams to develop solutions that balance reusability, maintainability,…