Solution Architect - AI Native SDLC Platform

About Vericence

Vericence is a digital engineering and technology consulting firm helping enterprises build AI-driven platforms, modernize legacy systems, and scale innovation through cloud, data, and intelligent engineering. We partner with global organizations to deliver high-impact technology solutions and build world-class engineering teams.

About the Role

We are building an AI-native platform that ingests a client's existing systems, user stories and source code, and delivers software features end to end with minimal human intervention. We need a senior Solution & Transformation Architect to own how this platform lands inside large enterprise clients.

You will sit between our product and engineering teams and client technology leadership. You will shape the solution blueprint, define the transformation levers across SDLC and STLC, and guide clients and internal teams through implementation across the platform's Context Graph, Event Orchestrator, Connectors and Agent Development modules. The role suits someone who has run large Quality Engineering, DevOps or core modernization transformations and now designs agentic AI solutions in enterprise settings.

Key Responsibilities

• Own the end-to-end solution blueprint for client implementations across all platform modules.
• Define transformation levers, operating model, implementation approach and data/context requirements for each client rollout.
• Map client SDLC and STLC processes (requirements, development, testing, release, operations) to agent-driven workflows, and quantify productivity and quality outcomes.
• Lead architecture reviews and trade-off decisions, and keep module leads aligned.
• Partner with the Principal Architect and Engineering Manager to turn product capabilities into scalable client solution strategies.
• Oversee integration architecture between client environments and the platform, including ALM, code repositories, CI/CD and enterprise toolchains.
• Set guidance on agent governance, observability, security and compliance for AI-native delivery in regulated industries.
• Run proofs of concept for emerging agentic capabilities such as agent runtimes, registries and gateways, and assess production readiness.
• Advise executive stakeholders on roadmap direction, adoption risk and value realization.
• Feed implementation learnings back into product and platform roadmaps, and mentor solution and delivery teams.

Required Qualifications

• 18+ years in software engineering, of which 6+ are in solution architecture, enterprise architecture or transformation leadership roles.
• Proven delivery of large-scale technology transformation for enterprise clients, such as Agile, DevOps or QE transformation, core platform modernization, or cloud migration.
• Deep understanding of SDLC and STLC, with Quality Engineering, test automation and CI/CD maturity models.
• Hands-on architecture experience with Generative AI and agentic systems in production or advanced pilot. This includes LLM-based applications, RAG, agent frameworks, and agent orchestration, governance and MLOps.
• Strong cloud architecture background, preferably AWS. Exposure to Bedrock, AgentCore or equivalent managed agent services is highly valued.
• Working knowledge of microservices, event-driven architecture, containers (Docker, Kubernetes) and infrastructure-as-code (Terraform).
• Experience leading solutioning across multiple concurrent client engagements in consulting, CoE or advisory roles, including pre-sales and solution engineering.
• Excellent executive communication, with the ability to set technology roadmaps alongside CXO and VP stakeholders.

Preferred

• Domain experience in Insurance (P&C or Life), Healthcare or Retail; core platform migration exposure (e.g. Guidewire) is a plus.
• Experience with knowledge graphs (Neo4j, Neptune) and vector stores (Pinecone, Weaviate, OpenSearch).
• Platform integrations with Jira, GitHub, Azure DevOps or enterprise ALM toolchains.
• Enterprise architecture tooling and frameworks such as LeanIX or TOGAF.
• Published frameworks, conference talks or open-source contributions in AI, agentic systems or Quality Engineering.