Data Scientist

On-siteMidAI Engineerposted 2w ago

✦Role summaryAI-generated

This role focuses on building and validating conversational agents using Sierra's Agent Studio and SDK, leveraging TypeScript/JavaScript to implement complex agent behaviors. Candidates will integrate these agents with enterprise APIs and contact‑centre systems, ensuring robust, production‑ready deployments.

Skills required

Dialogflow Rasa LLMs TypeScript JavaScript Agent studio Api integration Sierra AI Agentic AI CI/CD Git Data analysis Data science

About this role

Band B1 / B2 — Sierra Engineer

Experience

4–6 years total in Conversational AI / agentic or virtual-assistant development (e.g., Dialogflow CX, Amelia, Kore.ai, Rasa, LLM-based agents).
1–2 years hands-on Sierra, with at least one delivered/production Sierra project.
Strong programming background in TypeScript / JavaScript.

Core Responsibilities

Build and validate agents in Agent Studio using Journeys, Conditions, Rules, Policies, Tools, and Configuration.
Implement code-defined agent behaviour in the Agent SDK (TypeScript/JSX on Sierra's custom runtime) using hooks, tool registration, and lifecycle events.
Author instruction-driven and scenario-based simulations (Studio and code) and keep them stable and meaningful.
Integrate agents with APIs and enterprise/contact-centre systems, including transfer/routing to queues and numbers with attached data.
Participate in workspace-based development: merge requests, snapshots, and workspace reviews.
Execute releases under guidance, respecting critical-simulation gates and approvals.
Contribute to CI/CD (e.g., GitHub Actions) — running simulations and wiring release checks.

Mandatory Skills (per Sierra platform)

Agent Studio: conversational design, customer journeys, and the block-based editor (Journeys, Conditions, Rules, Policies, Tools, Configuration).
Agent SDK: TypeScript/JavaScript with JSX; SDK APIs (hooks, tool registration, lifecycle events); code-defined simulations; workspace/snapshot/release concepts.
Simulations: writing instruction-driven and scenario-based tests; critical-simulation enforcement; stabilising flaky simulations.
Git & CI/CD: branching, merging, code reviews; running simulations and release enforcement in pipelines.

Good to Have

Ghostwriter (AI-assisted authoring): prompting, reviewing, validating, and merging AI-generated journeys/simulations.
Basic observability (traces/alerts) and analytics (CSAT, resolution/containment rate).
Contact-centre / IVR and telephony transfer (Genesys or similar) exposure.

Responsibilities

Design and develop data models and analytical solutions, Optimize data analysis processes and methodologies, Ensure data solutions meet business and technical requirements, Provide technical support for data science projects, Collaborate with stakeholders to address data science needs.