Technical Architect - Agentic AI

Key Responsibilities

• Build core platform components microservices (FastAPI), agent workflows, RAG pipelines, and integrations — working closely with the Solution Architect on design decisions.
• Guide and review the work of 2–3 junior engineers; unblock them on implementation issues and enforce coding/design standards.
• Translate customer requirements into technical POCs and MVPs; demo working solutions to prospects and clients.
• Support presales: solution scoping, effort estimation, technical proposal/RFP inputs, and architecture walkthroughs in client conversations.
• Stay current with the fast-moving agentic AI and LLM landscape and bring relevant patterns/tools back into the platform.
• Troubleshoot issues across the stack — from agent logic down to infra/data layer — during build and pilot phases.

Must-Have Skills

• 10+ years in software engineering, including at least 1.5–2 years of hands-on work building LLM-powered or agentic applications (RAG, agent orchestration, tool/function calling, MCP, or similar — exact frameworks used may vary by past employer).
• Strong Python and experience building/maintaining microservices (FastAPI or equivalent).
• Practical exposure to at least one workflow orchestration engine (Temporal, Airflow, Camunda, or similar) — deep Temporal expertise is a plus, not a prerequisite.
• Working knowledge of vector and/or graph data stores (e.g., Qdrant, Pinecone, pgvector, Neo4j) and event streaming (Kafka or equivalent).
• Comfortable with containers/Kubernetes (any flavor — k3s, EKS, AKS, GKE) and standard CI/CD practices.
• LLM ecosystem familiarity: multi-provider usage (OpenAI, Anthropic, Bedrock, or Vertex AI), prompt/agent evaluation basics, and awareness of observability tooling for LLM apps (Langfuse, Phoenix, LangSmith, or similar).
• Prior exposure to presales/POC work — scoping, estimating, and demoing technical solutions to customers.
• Strong verbal and written communication; confident presenting to client stakeholders, including technical and semi-technical audiences.
• Experience informally leading or mentoring a small engineering team (2–3 engineers).

Good to Have

• Exposure to API gateways/service mesh (Kong, Linkerd, Istio) and identity/auth stacks (Keycloak, Ory, or similar).
• Background in IT services / consulting delivery model.
• Familiarity with GraphQL, hybrid search (vector + keyword + graph), or SRE/DevOps automation use cases.
• Prior contribution to internal accelerators, reusable frameworks, or platform tooling.