About this role
We’re looking for a Senior Artificial Intelligence Engineer to help us ship production-grade LLM applications with speed, pragmatism, and strong engineering habits. You’ll build AI systems that plug into real business workflows: retrieval, agents, automation, APIs, and observability, so they’re not just demos, but reliable products that deliver measurable ROI.
WHAT YOU’LL DO
- Build and ship LLM-powered features end-to-end: from prototype to production-ready systems (RAG, agents, tool calling, workflow automation)
- Design retrieval and search pipelines using OpenSearch / Elasticsearch, including indexing strategies and query patterns that work for real user needs
- Develop backend services and APIs in Python, using Pydantic for robust data validation and clear contracts
- Orchestrate async and scheduled workloads (batch jobs, pipelines, background workers) with Celery / Prefect
- Own data modeling and persistence for AI workflows using SQLAlchemy
- Add observability and reliability with OpenTelemetry: tracing, metrics, and logs that make systems debuggable and safe to operate
- Collaborate async-first with product and engineering: align on trade-offs, ship continuously, improve based on feedback and usage
- Proactively identify edge cases and failure modes (hallucinations, retrieval misses, long-tail inputs, timeouts) and fix them with pragmatic engineering
TECH STACK
- Python (Pydantic, SQLAlchemy)
- LLM stack: OpenAI SDK, LangChain / LangGraph
- Search/Retrieval: OpenSearch / Elasticsearch
- Orchestration: Celery / Prefect
- Observability: OpenTelemetry
WHAT WE’RE LOOKING FOR
- Strong software engineering fundamentals with excellent Python (clean architecture, testable code, API design)
- Practical experience building LLM applications in real contexts (RAG, agents, tool calling, workflow automation)
- Comfort integrating AI into business processes: you care about reliability, UX constraints, and operational realities, not just model outputs
- Ability to handle multiple tasks and quickly re-prioritize without losing clarity or quality
- Clear and consistent communication in a fully remote team (async-first)
NICE TO HAVE
- Experience with LLM evaluation, guardrails, and quality measurement (test suites, regression checks, prompt/versioning strategies)
- Experience with BS4 and/or Playwright for scraping, data extraction, or automated validation flows
- Familiarity with practical security/privacy considerations in AI systems (PII handling, data retention, access control)
LET US KNOW
- Your portfolio (GitHub, demos, blog posts, talks, anything that shows what you’ve built)
- (Optional) A couple of AI-enabled products you shipped and what you owned (retrieval design, orchestration, APIs, eval/guardrails, observability, etc.)
WHAT YOU’LL DO
- Build and ship LLM-powered features end-to-end: from prototype to production-ready systems (RAG, agents, tool calling, workflow automation)
- Design retrieval and search pipelines using OpenSearch / Elasticsearch, including indexing strategies and query patterns that work for real user needs
- Develop backend services and APIs in Python, using Pydantic for robust data validation and clear contracts
- Orchestrate async and scheduled workloads (batch jobs, pipelines, background workers) with Celery / Prefect
- Own data modeling and persistence for AI workflows using SQLAlchemy
- Add observability and reliability with OpenTelemetry: tracing, metrics, and logs that make systems debuggable and safe to operate
- Collaborate async-first with product and engineering: align on trade-offs, ship continuously, improve based on feedback and usage
- Proactively identify edge cases and failure modes (hallucinations, retrieval misses, long-tail inputs, timeouts) and fix them with pragmatic engineering
TECH STACK
- Python (Pydantic, SQLAlchemy)
- LLM stack: OpenAI SDK, LangChain / LangGraph
- Search/Retrieval: OpenSearch / Elasticsearch
- Orchestration: Celery / Prefect
- Observability: OpenTelemetry
WHAT WE’RE LOOKING FOR
- Strong software engineering fundamentals with excellent Python (clean architecture, testable code, API design)
- Practical experience building LLM applications in real contexts (RAG, agents, tool calling, workflow automation)
- Comfort integrating AI into business processes: you care about reliability, UX constraints, and operational realities, not just model outputs
- Ability to handle multiple tasks and quickly re-prioritize without losing clarity or quality
- Clear and consistent communication in a fully remote team (async-first)
NICE TO HAVE
- Experience with LLM evaluation, guardrails, and quality measurement (test suites, regression checks, prompt/versioning strategies)
- Experience with BS4 and/or Playwright for scraping, data extraction, or automated validation flows
- Familiarity with practical security/privacy considerations in AI systems (PII handling, data retention, access control)
LET US KNOW
- Your portfolio (GitHub, demos, blog posts, talks, anything that shows what you’ve built)
- (Optional) A couple of AI-enabled products you shipped and what you owned (retrieval design, orchestration, APIs, eval/guardrails, observability, etc.)
Tech stack
LLMPython
About NESSO LABS S.R.L
NESSO LABS S.R.L is hiring for the ai engineer (senior) - full-time role. NewJob aggregates active openings directly from NESSO LABS S.R.L's applicant tracking system, so this listing is current.
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