Python AI Engineer Job Central area,Louisiana USA,Software Development

5 + years of commercial software engineering, of which at least 12 months building LLM or agent systems that reached production. Demos and pilots do not count.
Strong production Python: typing, tests, packaging, async, API clients, error handling. Able to own a service end to end.
Strong SQL and comfort with a columnar warehouse. Snowflake preferred, any equivalent accepted.

Requirements:

ENGINEERING

5 + years of commercial software engineering, of which at least 12 months building LLM or agent systems that reached production. Demos and pilots do not count.
Strong production Python: typing, tests, packaging, async, API clients, error handling. Able to own a service end to end.
Strong SQL and comfort with a columnar warehouse. Snowflake preferred, any equivalent accepted.

AGENT ENGINEERING

Tool and function calling, MCP servers and clients, orchestration and state, retries and fallbacks, structured output, retrieval and context assembly, human-in-the-loop and approval flows.
Evaluation as a first-class skill: building golden sets from real cases, regression suites, judges, measuring against a baseline instead of asserting quality.
Observability: tracing, structured logging, token and cost telemetry, dashboards and alerts.

MICROSOFT

Hands-on Azure work inside a customer tenant: resource groups, managed identities, Key Vault, App Service or Container Apps or Functions, basic networking.
Azure AI Foundry or Azure OpenAI in production.
Azure Dev Ops: repos, pipelines, work items. Able to set up CI/CD from scratch.

WAYS OF WORKING

Forward-deployed or embedded experience, or clear readiness for it: sitting inside a client team, running working sessions with business users, demonstrating on real cases every week, taking questions at a phase-gate review.
Brownfield mindset. Wraps, connects and improves what already works instead of proposing a rewrite.
Worked in a regulated or otherwise controlled environment: security review path, access control, audit trail, data boundaries, handling of sensitive data.
Explains a failure as clearly as a result. Writes short, readable updates.
English C1 or better, spoken and written. Daily client contact in English.
EU residence, within one hour of CET, able to travel to Rotterdam every two weeks.
Full-time availability on one client, no parallel projects.

Nice-to-Have:

Financial services domain: asset management, fund or client onboarding, KYC and AML, trade processing, reconciliation, client reporting, or payments.
Snowflake specifics:
Cortex, semantic models, dbt, data products, MCP over Snowflake.
Microsoft certifications: AI-102, AZ-204, DP-203 or Azure Solutions Architect Expert.
Copilot Studio, Power Platform, Microsoft Graph, Semantic Kernel or Auto Gen.
Experience turning business-user scripts into governed services, working alongside citizen developers or business technologists rather than a professional engineering team.
Knowledge graph and ontology work, Neo4j, data lineage tooling.
Compliance evidence work: DORA, EU AI Act, ISO 27001, SOC 2.
Data engineering: orchestration (Airflow or similar), event-driven pipelines, file-based integrations.
.NET or C# for integration inside Microsoft-heavy estates.
Enablement experience: running trainings, pairing with non-engineers, writing playbooks and runbooks.
Dutch language.

Responsibilities:

DELIVERY, THE FIRST TWELVE WEEKS

Frame (weeks 1 to
2). Map the value stream with the client team. Inventory systems of record, the 120-plus Python tools, data lineage and access. Agree the baseline, the real cases and the success criteria. Propose the deployment architecture for the client architecture team to approve.
Prove (weeks 3 to
6). Build the smallest useful slice on real data, hosted in the client tenant, with visible assumptions, traces,…