About This Role
As a Causal AI Engineer, you will play a critical role in building and validating AI systems that power real-world manufacturing operations. This is a hands-on engineering position supporting a live enterprise engagement, focused on ensuring the correctness, reliability, and explainability of AI-driven decision-making using industrial and SCADA data.
This role goes beyond traditional machine learning implementation. You will work at the intersection of causal inference, statistical validation, AI evaluation, and production monitoring, helping build AI systems that can be trusted in high-impact operational environments. Working closely with AI Engineers, Data Scientists, Software Engineers, and Manufacturing SMEs, you will design evaluation frameworks, validate causal reasoning pipelines, detect performance drift, and ensure statistical rigor across AI-powered solutions.
What You Will Do
- Own the correctness, validation, and reliability of AI pipelines operating on manufacturing and SCADA data
- Design and maintain causal graph structures and support causal reasoning workflows for industrial AI systems
- Build statistical evaluation frameworks to validate AI outputs and business assumptions
- Apply hypothesis testing, control chart analysis, correlation analysis, and time-series techniques to investigate system behaviour and model performance
- Design and implement automated evaluation pipelines with datasets, metrics, dashboards, and CI/CD integration
- Monitor model performance, detect drift, and proactively identify quality issues in production AI systems
- Build data quality and model monitoring systems, including validation datasets and testing frameworks
- Collaborate with engineering and data teams to reproduce findings and validate system claims using quantitative methods
- Develop data validation and quality assurance processes for AI-powered applications
- Build dashboards and reporting mechanisms for AI performance monitoring
- Contribute to the development of scalable AI evaluation infrastructure and best practices
What You Need to Succeed
- Strong proficiency in Python and SQL
- Experience working with PostgreSQL and at least one of MSSQL or MySQL
- Hands-on experience with data analysis libraries such as Pandas and NumPy
- Experience building evaluation, testing, or validation pipelines for AI, ML, or data-driven applications
- Familiarity with metrics-driven engineering and production-quality validation frameworks
- Mandatory applied statistics expertise: hypothesis testing, control charts and Statistical Process Control (SPC), correlation vs. partial correlation analysis, time-series analysis fundamentals, and experimental validation techniques
- Comfortable challenging assumptions and validating engineering claims using statistical evidence and calculations, not just implementing models
What Will Give You an Edge
- Experience in manufacturing analytics, industrial AI solutions, SCADA systems, process industries, production operations, quality engineering, or AI validation and evaluation platforms
- Exposure to Causal AI, causal graphs, or causal inference concepts
- SPC / MSPC (Multivariate Statistical Process Control)
- Experience with LLM evaluation frameworks such as LangSmith, Ragas, or Promptfoo
- Experience with Docker and containerised deployments
- Exposure to Google Cloud Platform (GCP) and production ML or LLM systems
What Qfyre Offers
- Hands-on ownership of AI validation and evaluation infrastructure on a live, real-world manufacturing engagement, not a research sandbox
- Direct collaboration with AI Engineers, Data Scientists, and Manufacturing SMEs on production-grade industrial AI systems
- Depth in causal inference and statistical rigor, a discipline still rare across most AI engineering teams
- Hybrid work model based in Bengaluru
Skills and Technologies
PythonSQLPostgreSQLPandasNumPyCausal InferenceSPCTime-Series Analysis
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