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Responsibilities • Define and execute comprehensive test strategies covering statistical, ML, LLM and agentic AI models. • Perform functional, regression and scenario ‑ based testing of model behaviours and workflows. • Conduct AI/ML evaluations including accuracy checks, bias/fairness assessment, robustness analysis and drift detection. • Assess end ‑ to ‑ end model workflows including data inputs, feature transformations, task completion, tool ‑ use accuracy and multi ‑ step reasoning. • Design and maintain automated test and evaluation pipelines, including benchmarking and regression frameworks. • Validate API and tool ‑ integration behaviour in production ‑ like environments, identifying dependency or orchestration issues. • Diagnose issues using observability, logging, tracing and debugging tooling, and document findings clearly. • Collaborate with data scientists across departments to understand modelling intent, feature logic and expected behaviours. • Perform data ‑ management tasks to support AI/ML model testing, including maintaining metadata, documenting key datasets and ensuring clarity of data inputs. • Contribute to AI/ML proof ‑ of ‑ concept (POC) initiatives to strengthen evaluation methodologies and support innovation. • Support data‑management/analytics initiatives such as the Analytics Workbench and contribute to AI/ML/data analytics enablement. Requirements • Minimum 4 years of relevant experience in model testing, QA/QC, AI/ML evaluation, CI/CD, MLOps, data engineering, or related technical roles. • Proficiency in Python (especially PySpark, MLlib, pytest), R and SQL; knowledge of Scala, Rust, Java, JS or C++ is a plus. • Experience designing and executing test strategies for ML/AI models, including automated pipelines and regression frameworks. • Ability to evaluate statistical, ML and LLM models using performance, bias, robustness and drift metrics. • Strong ability to assess feature engineering logic, dataset integrity, workflow reliability and tool ‑ integration behaviours. • Experience troubleshooting using logs, traces and debugging tools to identify root ‑ cause issues. • Strong documentation and communication skills to articulate findings, risks and remediation requirements. • Ability to collaborate effectively with data science, engineering, IT and governance functions. • Understanding of Responsible AI concepts and quality expectations for production ‑ ready AI/ML systems.
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.