Senior AI & Data Platform Engineer

Interested in this role?

Altura is looking for a hands-on Senior AI & Data Platform Engineer to develop the data and conversational AI components of an existing enterprise SaaS analytics platform.

You will take ownership of the Big Query analytical layer, governed data APIs, and a production-ready AI analytics agent, collaborating directly with our technical leadership and another senior engineer.

• Review and extend existing Big Query Bronze, Silver, and Gold data layers.
• Implement multi-tenant Row-Level Security and PII masking.
• Define governed semantic metrics and typed analytical APIs.
• Reconcile business KPIs against existing Power BI reporting.
• Build an AI analytics agent using Vertex AI or equivalent.
• Implement function calling, Pydantic / JSON Schema, and structured JSON outputs.
• Manage AI sessions with Redis and secure tenant isolation.
• Integrate forecasting services and analytical APIs.
• Create automated evaluations, integration tests, and technical documentation.
• Ensure the LLM cannot generate or execute SQL directly.

• 5+ years of experience in Python, backend, and data engineering.
• Strong Big Query, SQL, and Google Cloud Platform skills.
• Proven experience implementing LLM agents in production.
• Experience with function calling, Redis, APIs, and Pydantic.
• Familiarity with semantic layers, dimensional modeling, and dbt/Dataform.
• Understanding of SaaS security, access controls, and data governance.
• Professional English communication skills.
• Ability to work independently and meet agreed technical milestones.

• Duration: 12 weeks.
• Total compensation: USD 3,000.
• Payment schedule: 20% / 30% / 30% / 20%, upon accepted milestones.
• Remote and flexible schedule, with agreed coordination meetings.
• Complete source code, automated tests, documentation, and handover required.
• Contract scope and technical acceptance criteria agreed before the start.

Altura is an AI technology partner for companies that want technology to work inside their operation, not alongside it.

We start by understanding the business. Before proposing anything, we map how the company actually runs: its processes, its systems, its data, and where the operational load sits. The goal is to find where technology has real leverage, and where it doesn't.

We build the roadmap. Opportunities are prioritized by return, effort and dependencies, each one with defined scope, estimated investment, and the indicator it is meant to move. The company ends up knowing what to do first, what it costs, and how success will be measured.

We implement. Custom software when existing tools don't cover what the operation needs. Automations that connect systems and remove manual work. AI agents that analyze information, decide and execute tasks within a process. And the infrastructure that makes all of it run reliably. We are not tied to a predefined solution — we choose the technology each business priority calls for.

We stay after go-live. Solutions reach production and keep being improved. As the operation evolves, we identify what comes next.

One team, from strategy through implementation and beyond. That is the whole point: technology projects usually break at the handoff, so we don't hand off.