Solution Architect: Ai Platforms

**Job Description
- Solution Architect: AI Platforms**

**About Codvo**

At Codvo, software and people transformations go hand-in-hand. We are a global empathy-led technology services company. Product innovation and mature software engineering are part of our core DNA. Respect, Fairness, Growth, Agility, and Inclusiveness are the core values that we aspire to live by each day. We continue to expand our digital strategy, design, architecture, and product management capabilities to offer expertise, outside-the-box thinking, and measurable results.

**The Role**

We are seeking an experienced and visionary Solution Architect to join our dynamic team. As a Solution Architect, you will be the primary technical bridge between our clients' most complex business challenges and the groundbreaking capabilities of our AI platform. You will be responsible for designing robust, scalable, and innovative solutions that leverage our platform's MCP-first architecture to deliver transformative business value.

This is a key role that requires a blend of deep technical expertise, strategic thinking, and exceptional client-facing communication skills. You will be instrumental in demonstrating the power of our platform and ensuring our clients' success.

**Key Responsibilities**
- Client Engagement & Discovery: Collaborate closely with enterprise clients to understand their business processes, pain points, data ecosystems, and strategic goals.
- Solution Design & Architecture: Design end-to-end solutions by mapping client requirements to our platform's offerings (e.g., Knowledge Hubs, Conversational AI, Real-time Insights, and Agentic Automation). Create detailed architecture documents, data flow diagrams, and technical specifications.
- MCP Workflow & Ontology Design: Architect the core intelligence for client solutions. This includes designing client-specific ontologies and defining the structure, logic, and composition of Model Context Protocols (MCPs) to automate complex reasoning and workflows.
- Technical Leadership: Serve as the deep technical expert on the platform for both clients and internal teams. Provide guidance on best practices for data integration, RAG implementation, and agentic automation.
- Proof of Concept & Prototyping: Lead the technical design and development of Proof of Concept (PoC) projects to demonstrate the feasibility and value of our solutions for specific client use cases.
- Cross-functional Collaboration: Work closely with our Engineering teams to ensure proposed solutions are feasible, scalable, and align with the platform's core architectural principles. Partner with the Product team to provide feedback from the field that informs the future roadmap.
- Implementation & UAT Guidance: Provide architectural oversight and technical guidance during the implementation and User Acceptance Testing (UAT) phases of client projects, ensuring the delivered solution meets the design specifications and business requirements.
- Continuous Learning: Maintain a deep understanding of the rapidly evolving AI landscape, including new LLM capabilities, agentic frameworks, and industry best practices, and integrate this knowledge into solution designs.

**Required Qualifications & Skills**
- Experience: 8+ years of experience in a technical role such as Solution Architecture, Enterprise Architecture, or senior-level Software Engineering, with at least 3+ years in a client-facing capacity.
- Architectural Expertise: Proven ability to design and document complex, distributed, and scalable software systems. Deep understanding of microservices, event-driven architecture, and MCP-first design principles.
- AI/ML Acumen: Strong foundational knowledge of modern AI concepts, including Large Language Models (LLMs), Retrieval Augmented Generation (RAG), and vector databases (e.g., Qdrant, Pinecone, Weaviate).
- Data Integration: Hands-on experience designing data pipelines and integrating with diverse enterprise data sources (e.g., relational databases like PostgreSQL, unstructured document stores like SharePoint, APIs, and data warehouses).
- Cloud Proficiency: Experience architecting solutions on a major cloud platform (AWS, Azure, or GCP).
- Technical Foundation: Proficiency in Python is strongly preferred. Familiarity with containerization (Docker) and orchestration (Kubernetes) is essential.
- Communication: Exceptional verbal and written communication skills, with the ability to explain complex technical concepts to both technical and non-technical audiences.

**Preferred Qualifications (Nice to Have)**
- Direct, hands-on experience designing or building systems using agentic AI frameworks (e.g. LangGraph, CrewAI).
- Knowledge of semantic web technologies, including ontologies (OWL/RDF) and graph databases (e.g., Neo4j).
- Familiarity with the concepts behind the Model Context Protocol (MCP) or similar advanced agentic architectures.
- Experience with modern observability stacks, particularly OpenTelemetry.
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