JD below:
About the Role
We're looking for a Senior AI Engineer to design, build, and deploy Python-based agentic automations for simple-to-medium complexity business tasks, end-to-end from design through testing and deployment. Some of this runs on GCP, so hands-on exposure to the platform is useful, though deep cloud expertise isn't the bar — we care most about strong Python and agentic solution design skills. From time to time you'll also help convert an existing RPA automation into a Python-based agentic solution, but that's a small slice of the work — the core of the role is building new automations.
Key Responsibilities
- Design, develop, and deploy new Python-based automations and agentic workflows for simple-to-medium complexity business tasks, end-to-end from requirements through testing and deployment.
- Build and integrate agentic AI components (LLM-based agents, orchestration/tooling frameworks) into automations, choosing the right level of agentic-ness for the task at hand.
- Deploy and operate automations on Google Cloud Platform (GCP) where needed, integrating with APIs, databases, and enterprise systems.
- Write and execute automated unit and integration tests, and run these through CI/CD pipelines to ensure reliable deployments and mitigate production risk.
- Diagnose and resolve technical defects in your automations, optimizing performance and stabilizing processes before handoff to support teams.
- On occasion, assess an existing RPA/UiPath automation and convert it into a Python-based agentic solution — a minor, occasional part of the role rather than the main focus.
- Act as a technical guide for junior and mid-level engineers within your pod; run peer code reviews to enforce coding standards and reusability.
- Serve as a technical representative in client meetings — translating technical approach, trade-offs, and risk into language accessible to non-technical stakeholders.
- Collaborate with cross-functional stakeholders — data engineers, DevOps, product managers, and business analysts — to prioritize and deliver the automation roadmap.
- Stay current on industry trends in agentic AI and automation, and continuously improve our delivery methodology and service offerings.
Required Qualifications
- 5+ years of hands-on Python software engineering experience, including building production services, APIs, and data pipelines.
- Experience building or integrating agentic AI automations (e.g. LLM-based agents, orchestration frameworks) for simple-to-medium complexity business tasks.
- Strong analytical and solution design skills — able to take a business process description and design a clean, efficient Python/agentic automation for it.
- Experience with common Python data/engineering libraries (e.g. Pandas, NumPy) and building well-tested, maintainable code.
- Proficiency with containerization (Docker/Kubernetes) and CI/CD pipelines.
- Some hands-on exposure to Google Cloud Platform (GCP) — e.g. Compute Engine, Cloud Run, Cloud Storage, or similar. Deep GCP expertise is not required; we're looking for enough familiarity to work in it and pick up more as needed.
- Strong track record operating in Agile/Scrum frameworks within enterprise engagements, working alongside BAs, developers, and business SMEs.
- Excellent communication and stakeholder management skills — able to articulate technical challenges and solutions to both technical peers and project leadership.
- Cloud Deployment Experience: Deploy and manage Python-based applications and agentic solutions using Kubernetes, including GKE, and Google App Engine.
Nice to Have
- Hands-on experience with UiPath (Studio, Orchestrator, REFramework) or other RPA platforms (Automation Anywhere, Power Automate) — useful for the occasional RPA-to-agentic conversion.
- Exposure to UiPath AI Center / Action Center, Agentic AI solution design, or UiPath Advanced RPA Developer certification.
- Exposure to consulting environments or client-facing delivery roles.
- Familiarity with additional cloud platforms (AWS, Azure) beyond GCP.
- Interest in upskilling in adjacent technologies such as process mining (Celonis, UiPath Process Mining), low-code platforms (Power Apps), or conversational AI (Cognigy, Druid).
