ML Engineer – Experimentation Platform

Job Title: ML Engineer – Experimentation Platform
Experience: 3 – 4 Years

Location: Remote
Notice Period: Immediate Joiners Only

About the Role
We are looking for a highly skilled ML Engineer to join our Test & Learn Platform team. In this role, you will build and scale experimentation and causal inference services that enable business teams to make data-driven decisions globally.

You will work across statistical modeling, API development, cloud-native infrastructure, and large-scale data processing to deliver reliable and production-ready ML solutions.

Key Responsibilities

• Develop and maintain statistical and machine learning modules for:
  
• Difference-in-Differences (DID)
  
• Synthetic Control
  
• A/B Testing
  
• Multi-Treatment Effects
  
• Build and extend RESTful APIs using FastAPI and integrate them with web applications through SDK wrappers
  
• Design and optimize large-scale data pipelines using PySpark, Delta Lake, and Azure Data Lake
  
• Diagnose and resolve Out-of-Memory (OOM) issues in PySpark workloads by optimizing:
  
• Memory allocation
  
• Partitioning
  
• Broadcast joins
  
• Caching strategies
  
• Spark configurations
  
• Deploy and manage Databricks workloads including notebooks, job clusters, and Delta Lake tables
  
• Containerize and deploy services using Docker, Kubernetes, and CI/CD pipelines
  
• Ensure code quality, testing, and security using PyTest, SonarCloud, and Snyk
  
• Collaborate closely with Data Scientists and Product teams to convert research concepts into scalable production systems
  
•  
  

Mandatory Skills

• Strong experience in Python (3.9+)
  
• Hands-on expertise in:
  
• PySpark & Spark Internals
  
• Databricks
  
• FastAPI / API Development
  
• Azure Cloud Platform
  
• Kubernetes & Docker
  
• PyTest
  
• Strong understanding of:
  
• DID
  
• Synthetic Control
  
• A/B Testing
  
• Hypothesis Testing
  
• Panel Data Methods
  
• Expertise in statistical and ML libraries:
  
• statsmodels
  
• scikit-learn
  
• SciPy
  
• Pandas
  
• NumPy
  

Technical Requirements

PySpark & Spark Internals

• Strong understanding of Spark memory model
  
• Executor tuning and shuffle optimization
  
• Diagnosing and resolving OOM errors
  
• Experience with:
  
• Broadcast thresholds
  
• Partition skew handling
  
• Spill-to-disk optimization
  
•  
  
• GC tuning
  

Databricks

• Hands-on experience with:
  
• Job orchestration
  
• Cluster configuration
  
• Notebook workflows
  
• Delta Lake optimization
  
• Z-ordering, compaction, and caching
  

Cloud & DevOps

• Azure Storage, Azure ML, and Azure Data Lake
  
• Docker-based containerization
  
• Kubernetes orchestration for ML workloads
  
• CI/CD pipeline integration
  

Testing & Quality

• Unit and integration testing using PyTest
  
• Familiarity with SonarCloud, Snyk, and GitHub Actions
  

Good-to-Have Skills

• Experience with Celery and Redis for async task orchestration
  
• Familiarity with Polars, PyArrow, or SQLAlchemy
  
• Background in econometrics or experimental design
  
• Experience with Spark UI profiling and performance benchmarking
  
• Knowledge of advanced CI/CD tooling and automation practices
  

Preferred Candidate Profile

• Strong analytical and problem-solving abilities
  
• Ability to work independently in a remote setup
  
• Excellent collaboration and communication skills
  
• Passion for building scalable ML and experimentation platforms
  

Tech Stack

Languages & Libraries: Python, Pandas, NumPy, SciPy, statsmodels, scikit-learn
Big Data: PySpark, Spark Internals, Delta Lake
Cloud & Platforms: Azure, Databricks, Azure Data Lake
APIs & Backend: FastAPI
DevOps: Docker, Kubernetes, GitHub Actions
Testing & Security: PyTest, SonarCloud, Snyk