Job Description & How to Apply Below
Location:
Mumbai – Santacruz (Kalina) / Mahape (Navi Mumbai)
Experience:
3–8 Years
Function: AI Scaling Platform
About the Role
We’re looking for a highly analytical and hands-on Experiment Engineer to design, build, and scale experimentation platforms that power data-driven decision-making. This role focuses on A/B testing infrastructure, deployment automation, and experimentation pipelines that enable teams to validate hypotheses, optimize models, and ship winning variations with confidence.
Key Responsibilities
- Design and build scalable A/B testing frameworks, including data splitting strategies and champion vs. challenger testing models
- Develop infrastructure for running experiments and canary deployments on Kubernetes or managed cloud services (EKS, GKE, AKS)
- Containerize application variations using Docker and orchestrate them via Kubernetes for reliable testing environments
- Formulate hypotheses, conduct trials, analyze outcomes, and document iteration results
- Integrate experimentation workflows into CI/CD pipelines (Jenkins, Git Lab CI, Git Hub Actions) for automated rollout of variations
- Define metrics and implement monitoring/logging systems to track experiment performance and troubleshoot issues in real time
- Collaborate with product managers, developers, data scientists, and stakeholders to define objectives and interpret results
- Enable policy or decision teams to evaluate model outputs and approve production rollouts
- Debug distributed system issues, optimize Docker images, and ensure efficient resource utilization
Required
Skills & Qualifications
- 3–8 years of experience, with at least 2 years in A/B testing platforms, MLOps, or similar production environments
- Strong hands-on experience with at least one major cloud provider (AWS, GCP, or Azure)
- Expertise in Docker and Kubernetes
- Proven experience building and maintaining CI/CD pipelines
- Strong Python programming skills and familiarity with ML frameworks (scikit-learn, Tensor Flow, PyTorch, XGBoost)
- Experience with data pipeline tools (Airflow, Prefect) and model/data versioning tools (Artifact Registry, Git LFS)
- Experience implementing observability solutions for infrastructure and ML performance
- Strong shell scripting skills
What Makes You a Great Fit
- Strong problem-solving mindset and analytical thinking
- Excellent communication and collaboration abilities
- Ability to explain complex technical concepts to diverse stakeholders
- Proactive, self-driven, and comfortable working in fast-paced environments
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