Role Description Altoros is staffing a Senior Support Engineer for a client engagement supporting an analytics platform. This is a full-stack and data-stack role: the engineer owns the platform shell, the ingestion and semantic layer, and a bounded amount of embedded analytics component upkeep. The role is commercial-model first: month one is an observe and define phase (baseline setting, onboarding), with the engagement shifting toward outcome-based delivery from month two (issue resolution time, defect reduction against baseline, availability targets). AI-augmented delivery is central to this role and one of its most important elements. Working AI-first with Claude Code is how a single engineer credibly covers this full remit. Altoros builds its delivery on Anthropic's professional courses and certification, and the engineer uses Claude Code across the full range of work — maintenance, bug-fixing, and data work, not just new development — operating inside the client's own Claude Code / AI-tooling accounts. Qualifications - AI-first delivery (core requirement): hands-on with Claude Code (or similar) / AI-assisted engineering across the full development lifecycle; Anthropic's professional courses and certification are a strong plus (or readiness to complete them) - Full-stack development experience, including a modern front-end framework (e.g. React / TypeScript), authentication/SSO implementation, and multi-tenant application architecture - Hands-on experience with Dagster for orchestration (or similar tools) - Strong DBT and SQL experience for data transformation - Experience with BigQuery and the Google Cloud Platform (GCP) data stack - Experience with Cube or a comparable semantic-layer / metrics-layer tool - Familiarity with embedded analytics tooling (Embeddable or similar), component customization, theming, SDK integration - Comfortable working independently and engaging directly with client stakeholders - Strong documentation discipline: Confluence, DBML/ER diagrams - Available to work core hours aligned to Chicago time (CT) Requirements - Maintain and extend authentication and SSO integration - Own navigation and application shell components (modern front-end framework, e.g., React / TypeScript) - Manage tenant and user management, including multi-tenancy considerations - Build and maintain data ingestion pipelines - Operate and extend orchestration workflows in Dagster - Develop and maintain transformation logic in dbt and SQL - Work with the data warehouse on Google Cloud Platform (GCP), primarily BigQuery - Maintain the semantic layer in Cube, including metric definitions and data modeling - Customize and update Embeddable component files pulled into the client's repo - Maintain theming and keep the Embeddable SDK current - Maintain centralized documentation in Confluence, including DBML/database diagrams - Capture ongoing knowledge for handover and continuity purposes - Work to defined outcome targets from month two: P1 issue resolution/mitigation within one business day, defect reduction against an agreed baseline, and business-hours availability once the client is live - Use spare capacity (when live issues don't consume the monthly band) on preventative maintenance, hardening, and onboarding new data sources/integrations Benefits - 80 hours/month, full remit across platform shell, data/semantic stack, and Embeddable upkeep - Backup coverage required for continuity during absences: candidate should be able to hand off context cleanly

Role Description We're looking for an AI Engineer to design and build the AI systems behind real, production-grade applications. This is applied engineering work: building agentic workflows, retrieval pipelines, and orchestration layers that integrate into business-critical environments and stand up to real-world use. You'll take AI capabilities from idea to deployment, owning the full path including evaluation, observability, and the operational concerns that determine whether a system survives contact with users. What You'll Do - Design, implement, and ship AI systems that automate work, support decisions, and unlock new workflows for enterprise clients. - Build multi-step agents and tool-using LLM systems. - Wire systems into structured and unstructured data sources. - Package systems into services that other engineers and applications can consume safely. - Own the evaluation, observability, and feedback loops to iterate with confidence. - Collaborate closely with engineers, strategists, and designers. - Engage in client conversations from early scoping through delivery. Qualifications - 3+ years of experience shipping AI-powered systems in production. - Background in computer science, math, physics, engineering, or another technical or scientific field. - Strong in Python and fluent in modern LLM engineering patterns: agents, MCP, skills, tool use, retrieval, structured outputs, and evals. Requirements - Real software engineering skills: CI/CD, testing, version control hygiene, and code quality. - Ability to balance accuracy, latency, cost, and maintainability. - Experience debugging production systems. - Comfortable wrapping AI logic into services for other engineers. How You Work - Treat AI as an engineering discipline with a focus on observability and maintainability. - Adaptable to a consulting environment with varying domains and constraints. - Ability to translate business problems into technical solutions. - Comfortable in client interactions without project manager interference. - Enjoy the fast-paced nature of the field, actively integrating new models, tools, and patterns. - Push back on ideas that don't make sense and care about building sustainable systems. Interested? If this sounds like a fit, apply with your CV and a short note on a system you've built that you're proud of. Bonus points for something we can actually look at!

• We are seeking a Conversational AI application developer to join our Solution Delivery team. • You will be part of a talented software team that delivers the latest in Conversational AI and Virtual Agent technologies, bringing advanced voice and chat-driven solutions to enterprises across the world. • Working with customers to analyze and understand business requirements; • Designing the application in line with established usability principles and business requirements; • Developing hybrid Conversational AI applications that blend agentic and deterministic, rule-based capabilities; • Implementing and testing the semantic models and the conversation flow, leveraging rule-based, ML-based, and GenAI-based technologies made available on Omilia's Cloud Platform (OCP). • Tasks include: preparing, augmenting, and annotating datasets for ML and agentic training; • Executing model testing to assess performance; • Developing semantic rules using dictionaries and regular expressions; • Managing RAG knowledge base configuration; prompt engineering; • Using JavaScript to code application logic; • Integrating Omilia applications with client systems via REST web services; • Test-driven development, unit and regression testing; • Analyzing real-life human-system dialogs using Omilia's Conversational Insights and analytics tools; • Testing early platform features to provide feedback; • Closely working with customers and other team members to debug and troubleshoot issues; • Supporting partners and customers, providing consultation on design and development practices; • Engaging in continuous collaboration with team members, communicating problems effectively, and sharing insights.

• Working with customers to analyze and understand business requirements; • Designing the application in line with established usability principles and business requirements; • Developing hybrid Conversational AI applications that blend agentic and deterministic, rule-based capabilities; • Implementing and testing the semantic models and the conversation flow, leveraging rule-based, ML-based, and GenAI-based technologies made available on Omilia's Cloud Platform (OCP). More detailed tasks include: preparing, augmenting, and annotating datasets for ML and agentic training; executing model testing to assess performance; developing semantic rules using dictionaries and regular expressions; managing RAG knowledge base configuration; prompt engineering; • Using JavaScript to code application logic; • Integrating Omilia applications with client systems via REST web services; • Test-driven development, unit and regression testing; • Analyzing real-life human-system dialogs using Omilia's Conversational Insights and analytics tools, assessing the overall system performance, and identifying improvement opportunities; • Closely working with customers and other team members to debug and troubleshoot issues; • Testing early platform features to provide feedback, communicating identified product improvement opportunities or partner/customer feedback to the product team for resolution; • Supporting partners and customers, providing consultation on design and development practices as well as assisting with issue resolution during day-2 operation; • Engaging in continuous collaboration with team members, communicating problems effectively, and sharing insights