Senior AI Engineer, Data Quality & Pipeline Automation
About this role
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)
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.
Key skills
Details
- Pay: Shared during the application process
- Company: Turing
- Location: Remote
- Eligible locations: Worldwide (no restriction)
- Platform: Turing
Frequently asked questions
How much does the Senior AI Engineer, Data Quality & Pipeline Automation role pay?
Turing does not publish a pay rate for this Senior AI Engineer, Data Quality & Pipeline Automation position. Pay is shared during the application process and depends on your experience and the scope of the work.
Is the Senior AI Engineer, Data Quality & Pipeline Automation role remote, and can I work from home?
Yes. Turing lists this Senior AI Engineer, Data Quality & Pipeline Automation position as fully remote, so you can work from home or anywhere with a reliable computer and internet connection.
Is Senior AI Engineer, Data Quality & Pipeline Automation full time or part time?
Turing lists this as a contractor remote role. Full details are in the role description above.
Which countries can apply for Senior AI Engineer, Data Quality & Pipeline Automation?
Turing lists this role with no country restriction. Check the Turing listing for current requirements before applying.
Do I need experience to apply for Senior AI Engineer, Data Quality & Pipeline Automation?
The requirements are listed in the role description above. Turing screens applicants through its own process, and relevant skills or credentials strengthen your application.
How do I apply for the Senior AI Engineer, Data Quality & Pipeline Automation role, and is it legit?
Use the apply button on this page to go straight to the official Turing listing. Turing is a real hiring marketplace and applying is free. No legitimate employer charges you to apply.
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