Technical Architect, Appnovation

• 7+ years full-stack or platform engineering experience with 2+ years in technical leadership or architecture roles
• ≥2 years hands-on GenAI: building production LLM applications, prompt engineering, integrating AI coding assistants and autonomous agent frameworks
• Multi-cloud expertise: GCP (Vertex AI) or AWS (Bedrock, Guardrails)
• Security-first approach: GenAI-specific threat modeling and guardrails implementation
• Client communication: Ability to facilitate technical discussions and create clear documentation, slides, and architectural diagrams
• Strong technical breadth: Polyglot coding (TypeScript/Node.js, Python, Java), AI ecosystem knowledge (LangChain, LangGraph), MLOps practices
• Prototyping mindset: Experience building proof-of-concepts and production-ready AI implementations
• An understanding of the following, or comparative, technologies:
• AI Platforms: LangChain, AutoGen, CrewAI, LangGraph, Pydantic AI, PyTorch, TensorFlow, Unsloth, Vertex AI, AWS Bedrock, Azure OpenAI, multi-modal AI
• Data & Vector Systems: Milvus, Pinecone, Qdrant, RAG pipelines, embeddings
• Development: AI coding assistants (Cursor, Cline, Windsurf, Claude Code), Next.js, Nuxt.js, React Native
• CMS & Infrastructure: Contentful, Sanity, Builder.io, Adobe AEM, Terraform, microservices

Desirable

• In this role, you will design and prototype cutting-edge AI solutions, implement cloud AI architectures, and help guide our continued evolution in AI-driven development practices
• This hands-on role combines deep technical expertise with the ability to translate complex AI concepts into business value for our diverse client base, working directly with clients to define solutions and deliver production-ready implementations
• AI Solution Architecture: Design and prototype multi-cloud reference architectures leveraging GCP, AWS, and others
• Agentic AI & Integration: Build and implement agentic-LLM patterns, RAG pipelines, and architect solutions with embeddings, vector databases, MCP servers, and AI coding tools
• Security & Compliance: Embed OWASP GenAI risk mitigations and security best practices across SDLC with AI observability and strong monitoring
• Client Engagement: Participate in discovery workshops, present solution architectures, and communicate technical concepts to stakeholders
• Hands-on Development: Prototype solutions, build proof-of-concepts, and implement production AI systems
• Delivery Support: Contribute to project estimation, scoping and technical specifications; uphold delivery KPIs (security, cost, performance)
• Team Collaboration: Work with and mentor engineering teams around AI solutions and orchestrated development practices
• Coordinate: Across internal teams and client stakeholders across timezones

• Please note that only those selected for an interview will be contacted