Lead AI Transformation / Agentic Systems Lead
14. 09. 2026
About SDK.finance
SDK.finance provides software infrastructure for companies building digital finance products. We are now transforming how our own business operates by applying AI engineering, agentic workflows, structured automation, and human-governed AI systems across sales, product, engineering, delivery, support, and internal operations.We are looking for a hands-on leader who can help us move from traditional software execution to an AI-enabled operating model where people and AI systems work together to produce reliable, evidence-backed business outcomes.
About the Role
The Lead AI Transformation / Agentic Systems Lead will own the architecture and execution of SDK.finance’s internal AI transformation.This is not a role for building isolated AI demos or simple chatbots. The mission is to redesign real business workflows into controlled AI-enabled systems with clear goals, ownership, data boundaries, tools, approvals, evaluation, observability, recovery, and measurable business impact.You will work directly with leadership, product, engineering, sales, support, and delivery teams to identify where AI can meaningfully improve how the company works, and then turn those opportunities into production-grade systems.
What You Will Do
- Lead the design and rollout of SDK.finance’s AI transformation strategy across key internal workflows.
- Map real business processes and identify where AI should assist, automate, simplify, or remove work.
- Define AI-enabled workflows with clear goals, owners, inputs, permissions, budgets, stop conditions, evaluation criteria, and human approval points.
- Decide when to use deterministic code, search, RAG, single-agent workflows, multi-agent systems, or human review.
- Design agentic systems that produce verifiable outcomes, not just generated text.
- Establish internal standards for AI workflow design, structured outputs, tool use, memory, retrieval, evaluation, security, and observability.
- Build or supervise the first production AI workflows for areas such as inbound lead qualification, proposal preparation, product research, support triage, documentation, and engineering workflows.
- Create evaluation frameworks for AI systems, including regression tests, negative cases, human review criteria, and business success metrics.
- Partner with engineers to design durable execution, approval queues, recovery logic, audit trails, model routing, and cost tracking.
- Help the company build internal AI capability through documentation, playbooks, training, and hiring.
What We Are Looking For
- Strong experience building production software systems, preferably in platform, backend, infrastructure, enterprise software, or developer tooling.
- Practical experience with LLM-based systems beyond simple prompting.
- Understanding of RAG, structured outputs, tool calling, model routing, evaluation loops, agent orchestration, and human-in-the-loop design.
- Ability to translate messy business processes into bounded technical systems.
- Strong judgment on when not to use AI or when not to add agentic complexity.
- Experience designing systems with state, permissions, retries, recovery, observability, and operational controls.
- Ability to work across engineering, product, sales, support, and leadership.
- Strong written communication and ability to create clear technical and operational playbooks.
- High ownership: able to move from discovery to architecture, prototype, rollout, measurement, and iteration.
Nice to Have
- Experience in fintech, payments, ledger systems, banking infrastructure, compliance-heavy software, or regulated environments.
- Experience with agent frameworks, coding agents, AI developer tools, or internal automation platforms.
- Experience with OpenAI, Anthropic, Azure OpenAI, LangGraph, LlamaIndex, pgvector, OpenTelemetry, Temporal, MCP, or similar technologies.
- Experience building internal platforms or transformation programs in software companies.
- Experience hiring or mentoring AI engineers.
Success in This Role Looks Like
- SDK.finance has a clear AI transformation roadmap tied to real business outcomes.
- The company has at least several production AI workflows that save time, improve quality, or reduce operational friction.
- AI systems are evaluated, observable, recoverable, and governed by clear human decision points.
- Teams understand how to work with AI systems without losing ownership, context, or accountability.
- AI transformation becomes an operating capability of the company, not a collection of experiments.