AI/MLOps — ML Platform Engineer to $4000

Responsibilities

Core (70%)  
 ● CI/CD for models, prompts, and data pipelines  
 ● Model / feature registry with lineage & governance  
 ● Eval harness, canary/rollback, security & secrets  
 ● Full-stack observability (latency, cost, drift)

Support (20%)  
 ● MLOps dashboards  
 ● Performance / cost tuning  
 ● Resilience testing

Cross-team (10%)  
 ● Security reviews  
 ● Change-board  
 ● Incident response

Requirements

 ● Applied ML/GenAI literacy: review DS code, operationalize models/agents with evals, guardrails, SLOs
 ● Strong Python: modular, testable code (pytest, black, pylint, CI)
 ● SWE best practices: Git/GitHub, code reviews, Docker, FastAPI/RESTful services
 ● At least Upper intermediate level of English
 

Jobs-To-Be-Done

 ● Deliver low-latency, SLO-compliant model/agent endpoints  
 ● Enable repeatable CI/CD for models, prompts, pipelines  
 ● Support safe rollouts & instant rollback  
 ● Maintain registry + full observability/reporting  
 ● Ensure reliable data/embedding/indexing pipelines

Impact

 ● Scale to 5–10 production client models per quarter  
 ● Enable 1–2 new productized releases per quarter  
 ● Boost PoC → production reliability & speed