AI Enablement Engineer

About the Role Taktile helps financial institutions make smarter, safer decisions with the power of AI. Internally, we believe the companies that win the next decade will be the ones that turn AI from a tool into a multiplier - embedded into how every team works, not just shipped as a product feature. We're looking for an AI Enablement Engineer to make this the reality at Taktile. This is a high-leverage, internal-facing engineering role: you won't be building features for our customers, you'll be building the tools, integrations, and workflows that make every Taktilian faster and better at their job - from engineers shipping code, to GTM teams responding to customers, to operations closing the books. You'll be the first hire dedicated to this mission, working closely with team leads across the company to identify, build, and scale the AI workflows with the biggest impact. Taktile is a hybrid company. This role requires working at least 3 days per week from one of our offices in Berlin, Iasi, or London. What you’ll do Find the highest-leverage workflows. Embed with teams across Engineering, GTM, Customer Success, Finance, and Operations to identify where AI can remove the most friction. Build internal tools and agents end-to-end that wire Taktile's SaaS stack (HubSpot, Notion, Linear, Gong) into our engineers' and operators' daily workflows. Own scope, design, implementation, rollout, and iteration. Evolve AI architecture based on internal customer needs and tangible, measurable improvements. Drive adoption, not just deployment. Run hands-on workshops, pair sessions, and office hours to teach teams how to actually use what you ship. Measure success in real adoption and time saved, not features delivered. Partner with Security and IT on governance. Apply least-privilege principles from day one. Build the guardrails, access scoping, and audit trails that let us move fast on AI without exposing customer data or production systems. Stay on the frontier. Know when new tools and models release, which ones are worth experimenting with, and bring the best of it back into the business. Requirements 3+ years of professional software engineering experience, with a track record of shipping production systems end-to-end. Production-grade coding ability in Python in cloud-native environment. Hands-on experience building with modern LLM tooling - agent frameworks, tool use, prompt engineering, MCP, evals. You've shipped something real, not just experimented in a notebook. Strong SaaS API integration experience - REST, webhooks, OAuth, idempotency, the messy realities of stitching real systems together. Exceptional communication and stakeholder skills with technical and non-technical operators alike. A bias for adoption over abstraction. You measure your work by who's using it next week, not by how elegant the architecture is. Fluent English, both written and spoken. Ideal, but not required Prior experience in a technical enablement, internal tools, DX, or platform engineering role. Experience integrating unstructured data into AI workflows. Familiarity with AI governance and data classification in a regulated industry. You've run workshops or internal trainings and enjoy doing them. Our Stack AI: Claude Programming Language: Python Data Platform: AWS, Snowflake, dbt, Terraform Internal Tools: Notion, Linear, HubSpot, Gong, Ramp