Solutions Architect

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! What We're Looking For: 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. Position Overview: 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. Key Responsibilities: 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, integration, cost, technical debt, scalability, and security Advise senior and executive stakeholders: Translate complex AI opportunities and architectural trade-offs into clear recommendations, solution proposals, roadmaps, and architecture decisions that enable informed business decisions What You're Looking For: If you're looking for an opportunity to work in a fast‑growing market, surrounded by talented, motivated, and global colleagues who thrive on helping clients meet their most pressing business goals, we are the company for you. If you're driven, passionate, and want to be a 'key player' in a company's growth, we invite you to make a difference with a company that's defined by its employees. We want you to be bold, take risks, and imagine a better way to work. We should talk if we just described you! About Us: With more than 25 years of experience, OZ's trusted, deep expertise in Azure Cloud Solutions, Power Apps Application Development, Intelligent Automation, Enterprise Application Integration, Azure Data Strategy, and Artificial Intelligence ensures clients get rapid, effective results. OZ is dedicated to creating a seamless blend between work and life, offering our team the flexibility to work hybrid and/or remotely. We provide competitive pay and a well‑rounded benefits package, including full health coverage, a 401K, and unlimited PTO. Our culture is built on collaboration, growth, and balance - you'll have the tools and support you need to thrive. Join a team that truly lives its values and invests in your success. Requirements 8-10+ yrs. experience in software engineering and architecting enterprise solutions, including production systems you personally helped build Architectural depth in one major cloud provider (Azure, AWS, or GCP) with hands-on familiarity in at least one other Demonstrated experience architecting generative AI solutions on an enterprise platform - Azure OpenAI / AI Foundry, AWS Bedrock or Google Vertex AI Proven design of complex RAG pipelines, including their evaluation and observability Experience designing agentic or multi‑agent systems: task decomposition, tool use, state and memory, guardrails and fallbacks Hands‑on experience with at least one orchestration framework (LangGraph, LangChain, Semantic Kernel, LlamaIndex or equivalent) and enough Python to prototype and review code credibly Working knowledge of AI security and governance: prompt injection, data protection, responsible AI controls, and at least one formal framework (NIST AI RMF or ISO/IEC 42001) Experience in DevOps, SDLC, containers, Kubernetes, microservices, event-driven and API design Knowledge of architecture principles and industry-standard frameworks; data architecture and integration experience across a diverse landscape of package and custom applications Experience working with customer data‑pertaining systems such as loyalty systems, CRM, and CDP; understanding of different datastore types and how data flows across systems Well‑versed in API management to monitor security, application connections, traffic, and standardization Experienced in sizing AI workloads for optimal performance, latency, and cost, with a thorough understanding of the underlying infrastructure Performs work with a high degree of latitude. Handles the most complex issues. Possesses expert knowledge of subject matter. Provides leadership, coaching, and mentoring to the team Excellent communication skills - able to hold a design position with senior engineers and explain the same trade-off to a non-technical executive without losing the substance Bachelor's degree in math, computer science, or a related tech field and 8 or more years of experience Preferred Qualifications: Multi‑cloud architectural experience across Azure, AWS, and GCP Data platform depth (Databricks, Snowflake) and Lakehouse architecture Model fine-tuning or instruction‑tuning strategy; small-language-model and edge inference for cost efficiency Agent identity and authorization design at enterprise scale Delivery experience in a regulated environment, including model risk management exposure Preferred certification in TOGAF, or professional-level cloud architecture certification (Azure Solutions Architect Expert, AWS Solutions Architect Professional, GCP Professional Cloud Architect) Published work, patents, conference speaking, or open‑source contribution in the AI space Competencies: Architectural fluency Use‑case selection and data readiness judgment Design expertise Technical expertise Trade‑off analysis Stakeholder influence Problem‑solving skills Benefits Competitive Pay 401K Health Coverage #J-18808-Ljbffr