Senior AI Engineer, Data Quality & Pipeline Automation
Quick facts
- Type
- Part-time
- Location
- Remote
- Hours
- 40+ hours / week
- Skills
- Python, SQL
- Platform
- Turing — 255 open jobs
- Typical pay at Turing
- $15–20/hour · median $15 (16 jobs on our site)
- Apply
- Free to apply — never pay to get AI training work
Build and deploy an autonomous agent for data pipeline oversight.
About Turing
Turing is one of the world’s leading AGI infrastructure companies, working with frontier AI labs to accelerate model development through high-quality training data, evaluations, and engineering talent.
Engagement Details
Compensation: Market, please provide a specific hourly rate expectation
Availability: 40 hours/week, with at least 6 hours of IST/day
Type: Independent contractor
Duration: Approximately 2 months
Start: 01-Nov-2026
Compensation: Market, please provide a specific hourly rate expectation
Availability: 40 hours/week, with at least 6 hours of IST/day
Type: Independent contractor
Duration: Approximately 2 months
Start: 01-Nov-2026
About the Role
We are looking for an engineer to build and deploy an intelligent, autonomous operations agent that supervises our data pipeline infrastructure around the clock. The agent will predict delivery delays before they occur, verify data quality using adaptive statistical baselines, and synthesize code-level fixes for routine operational failures. You will own the agent end to end, from design and evaluation to production reliability.
What You Will Do
- Predictive health and SLA forecasting: Build models that analyze in-flight pipeline progress, execution velocity, and resource consumption against historical benchmarks to forecast SLA breaches 1-2 hours ahead, triggering priority re-queueing or upstream escalation.
Bottleneck profiling and self-healing: Enable the agent to inspect execution DAGs and query profiles when jobs stall or degrade, isolate root causes, and autonomously create isolated branches that synthesize fixes for syntax errors, upstream schema changes, and column renames. Each fix is verified through dry-run assertions and submitted as a review-ready change request with full test results for human approval.
Statistical and semantic data quality forensics: Replace static thresholds with rolling time-series baselines that account for seasonality, day-of-week swings, and business close cycles, and profile categorical entropy, column distributions, and foreign key orphan rates to catch subtle corruption that row counts and schemas miss.
Lineage reconciliation and quarantine: Trace datasets from raw ingestion through marts and reporting views, run cross-tier checksums and consistency assertions before reporting cycles begin, and automatically quarantine partitions with severe violations so bad metrics never reach executive dashboards.
Conversational copilot and incident management: Generate root-cause incident briefs covering failure cause, affected downstream assets, and recovery steps to cut alert noise, and build a natural language interface for checking pipeline health, triggering selective partition re-runs, and analyzing data distributions.
Bottleneck profiling and self-healing: Enable the agent to inspect execution DAGs and query profiles when jobs stall or degrade, isolate root causes, and autonomously create isolated branches that synthesize fixes for syntax errors, upstream schema changes, and column renames. Each fix is verified through dry-run assertions and submitted as a review-ready change request with full test results for human approval.
Statistical and semantic data quality forensics: Replace static thresholds with rolling time-series baselines that account for seasonality, day-of-week swings, and business close cycles, and profile categorical entropy, column distributions, and foreign key orphan rates to catch subtle corruption that row counts and schemas miss.
Lineage reconciliation and quarantine: Trace datasets from raw ingestion through marts and reporting views, run cross-tier checksums and consistency assertions before reporting cycles begin, and automatically quarantine partitions with severe violations so bad metrics never reach executive dashboards.
Conversational copilot and incident management: Generate root-cause incident briefs covering failure cause, affected downstream assets, and recovery steps to cut alert noise, and build a natural language interface for checking pipeline health, triggering selective partition re-runs, and analyzing data distributions.
What We Are Looking For
Python (core): strong production-grade Python for automation, agent logic, testing, and data processing.
SQL (core): advanced SQL for profiling, validating, and troubleshooting data in warehouses such as Snowflake, BigQuery, or Databricks.
ETL/ELT and orchestration (core): hands-on experience building and operating pipelines with Airflow (or Dagster/Prefect) and dbt, and understanding of how failures originate and propagate.
Data quality, observability, and statistics: experience with tools like Great Expectations, Soda, or Monte Carlo, plus a solid grasp of anomaly detection, time-series baselines, and forecasting.
LLM/agent engineering: experience with tool calling, multi-step agents, and frameworks such as the Claude Agent SDK or LangGraph, with a focus on safety, evaluation, and reliability.
Python (core): strong production-grade Python for automation, agent logic, testing, and data processing.
SQL (core): advanced SQL for profiling, validating, and troubleshooting data in warehouses such as Snowflake, BigQuery, or Databricks.
ETL/ELT and orchestration (core): hands-on experience building and operating pipelines with Airflow (or Dagster/Prefect) and dbt, and understanding of how failures originate and propagate.
Data quality, observability, and statistics: experience with tools like Great Expectations, Soda, or Monte Carlo, plus a solid grasp of anomaly detection, time-series baselines, and forecasting.
LLM/agent engineering: experience with tool calling, multi-step agents, and frameworks such as the Claude Agent SDK or LangGraph, with a focus on safety, evaluation, and reliability.
Evaluation Process
AI interview (~25 minutes)
Practical code/AI evaluation exercise (~30 minutes)
Hiring manager interview (~20 minutes)
The practical exercise focuses on your ability to review and evaluate AI-generated code, not competitive programming or algorithm puzzles.
AI interview (~25 minutes)
Practical code/AI evaluation exercise (~30 minutes)
Hiring manager interview (~20 minutes)
Skills: Python, SQL, Data Engineering, Data Management, Data Governance
About Turing
Turing started as a marketplace for remote software engineers and now focuses on training and evaluating AI models for frontier labs. Most listings are for developers who write, review and debug code used to improve LLM coding ability.