Senior AI Engineer

At Mindera, we believe in building software that improves lives. As a Senior AI Engineer you own the machinery that makes our accelerators reliable and reusable: the agent harness, orchestration, the platform-agnostic core, the evaluation infrastructure, and the observability that tells us whether a change made things better or worse. You are the reason delivery teams can trust output from a non-deterministic system.

What you'll own:

• The reusable accelerator core and the multi-agent workflows built on it.
• Agent design across coding, planning, review, and testing agents, with tool-calling and human-in-the-loop where it earns its place.
• The evaluation pipelines and telemetry that make quality measurable and regressions visible.
• Reliability, cost control, and observability for agentic systems in real use.

At Mindera, we believe in building software that improves lives. As a Senior AI Engineer you own the machinery that makes our accelerators reliable and reusable: the agent harness, orchestration, the platform-agnostic core, the evaluation infrastructur...

Key Qualifications:

• At least 6 years of professional experience as an AI Engineer or in a similar role.
• Strong production software engineering: Python, testing, packaging, CI/CD, async, REST, Docker. You build products, not scripts.
• Real depth in LLM application engineering: prompt design, function calling, structured outputs, RAG, vector stores, with the Anthropic and OpenAI APIs.
• Evaluation-driven development as a core habit, not an afterthought. You treat evals as the source of truth.
• Experience building agentic systems end to end: systems that plan, write code, validate their own output, and improve over time.
• Familiar with the orchestration landscape (LangGraph, LlamaIndex, AutoGen, CrewAI) and opinionated about when a thin custom harness beats a framework.
• Working fluency with at least one cloud data platform and SQL: enough to build eval and verification infrastructure that your applied-data counterpart fills with domain rules. You do not need to be the deepest data expert in the room.

At Mindera, we believe in building software that improves lives. As a Senior AI Engineer you own the machinery that makes our accelerators reliable and reusable: the agent harness, orchestration, the platform-agnostic core, the evaluation infrastructure, and the observability that tells us whether a change made things better or worse. You are the reason delivery teams can trust output from a non-deterministic system.

What you'll own:

• The reusable accelerator core and the multi-agent workflows built on it.
• Agent design across coding, planning, review, and testing agents, with tool-calling and human-in-the-loop where it earns its place.
• The evaluation pipelines and telemetry that make quality measurable and regressions visible.
• Reliability, cost control, and observability for agentic systems in real use.

At Mindera, we believe in building software that improves lives. As a Senior AI Engineer you own the machinery that makes our accelerators reliable and reusable: the agent harness, orchestration, the platform-agnostic core, the evaluation infrastructur...

Key Qualifications:

• At least 6 years of professional experience as an AI Engineer or in a similar role.
• Strong production software engineering: Python, testing, packaging, CI/CD, async, REST, Docker. You build products, not scripts.
• Real depth in LLM application engineering: prompt design, function calling, structured outputs, RAG, vector stores, with the Anthropic and OpenAI APIs.
• Evaluation-driven development as a core habit, not an afterthought. You treat evals as the source of truth.
• Experience building agentic systems end to end: systems that plan, write code, validate their own output, and improve over time.
• Familiar with the orchestration landscape (LangGraph, LlamaIndex, AutoGen, CrewAI) and opinionated about when a thin custom harness beats a framework.
• Working fluency with at least one cloud data platform and SQL: enough to build eval and verification infrastructure that your applied-data counterpart fills with domain rules. You do not need to be the deepest data expert in the room.