AI Engineer (Senior) - Full-time
Nesso Labs • Indonesia
Posted: January 24, 2026
Job Description
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.)
Additional Content
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.)