Lead Agentic Solutions Architect

Sapsf

United States

On-site

USD 140,000 - 190,000

Full time

20 hours ago

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Job summary

Sapsf is seeking a Solutions Manager to lead the design and delivery of agentic business transformation solutions. You will embed with clients, frame processes as multi-agent workflows in Python, and prototype production-grade agents in days.

You will mentor a small team, drive DevSecOps practices, and ensure measurable ROI across client engagements. The role requires strong Python skills, hands-on experience with agent development kits, and the ability to translate complex business problems

Qualifications

Strong Python engineering; idiomatic, typed, tested and packaged code.
Hands-on agent building with at least one agent development kit.
Solid command of agent engineering: prompt engineering, context engineering, prompt caching, RAG / context graphs, tool / function calling, MCP, and multi-agent orchestration.
Eval-driven development: designing evaluation harnesses and measuring agent quality, safety and reliability.
Standards-based integration and DevSecOps: APIs, secure auth, CI/CD, observability and AgentOps.
Ability to conceptualize a business problem as an agent quickly, and operate effectively in ambiguous, customer-embedded settings.
Client-facing maturity: translates technical concepts for non-technical stakeholders and owns outcomes.
Experience mentoring or leading small engineering teams.

Responsibilities

Conceptualize fast: embed with stakeholders, frame a business process as an agentic solution, and stand up a working agent prototype in days, not weeks.
Build Business Transformation Agents: design and ship single-agent and multi-agent systems in Python using ADKs that automate and transform client workflows with measurable ROI.
Own efficiency as the scorecard: drive delivery efficiency and operational efficiency; shorter cycle times, less manual effort, higher accuracy, lower cost-to-serve.
Engineer the agent core: apply prompt engineering, context engineering, prompt caching, RAG / context-graphs retrieval, memory, tool / function calling, MCP integration and multi-agent orchestration.
Integrate to standards: connect agents into client ecosystems through proven integration patterns, standards-based APIs and secure authentication.
Make reusability and predictability the default: build reusable agent components, skills, tool libraries and templates; add guardrails so agent behaviour is predictable, safe and repeatable.
Prototype and iterate quickly: use the kit's scaffolding to prototype, then harden to production-grade, well-tested Python.
Run eval-driven development: build evaluation harnesses and test suites that measure agent correctness, safety and regression before anything ships.
Own AgentOps / DevSecOps: CI/CD for agents, versioning, observability and telemetry, shift-left security, and Responsible AI governance baked in from day one.
Run a continuous, adaptable feedback loop: feed production telemetry, evals and client feedback back into prompts, context and agent design.
Stay ahead of the curve: adopt evolving agent frameworks and patterns quickly, and bring field learnings back to the practice.
Lead and mentor: set technical direction for a lean team of 3 agent engineers, raise the engineering bar, and grow the pod's agentic capability.