Senior Machine Learning Engineer / Data Engineer ( preferred immediate

Role Overview

Key Responsibilities

• ML Model Deployment & Operations

• Productionize, monitor, and maintain machine learning models in a scalable and secure environment
• Implement CI/CD pipelines for model versioning, testing, and roll-outs
• Perform model performance tuning and continual retraining strategies

• Data Pipeline Development

• Design, build, and optimize scalable ETL/ELT pipelines using PySpark, Python, and SQL
• Integrate and stream data with Apache Kafka for real-time processing scenarios
• Orchestrate workflows with Airflow (or equivalent orchestration tools)

• Data Architecture & Governance

• Collaborate on the design of the enterprise data warehouse and data lake architectures
• Define data modeling standards and maintain data catalogs
• Ensure data quality, lineage, and compliance with governance policies

• Containerization & Infrastructure

• Containerize applications and services using Docker (and Kubernetes familiarity is a plus)
• Work closely with DevOps to automate deployments and manage infrastructure as code

• Cross-functional Collaboration

• Partner with Data Scientists, BI teams, and Product Owners to translate analytics requirements into technical solutions
• Mentor junior engineers and conduct code reviews to uphold engineering best practices

Required Qualifications

• Experience:

  8+ years in Data Engineering and/or Machine Learning Engineering roles

• Languages & Tools:

• Python (expert-level proficiency)
• SQL (advanced querying, performance tuning)
• PySpark (building distributed data processing jobs)
• Apache Kafka (pub/sub, stream processing)

• Containerization:

  Solid hands-on experience with Docker; familiarity with Kubernetes beneficial

• ETL & Data Warehousing:

  Proven track record designing and operating ETL pipelines; experience with commercial or open-source DW platforms (e.g., Snowflake, Redshift, Hive)

• Orchestration:

  Experience with workflow orchestration tools (Apache Airflow or equivalent)

• Soft Skills:

  Strong problem-solving, communication, and mentoring abilities

Preferred Qualifications

• Hands-on experience with ML frameworks (TensorFlow, PyTorch, Scikit-Learn)
• Exposure to cloud platforms (AWS, GCP, or Azure) and their managed data/ML services
• Knowledge of infrastructure-as-code tools (Terraform, CloudFormation)
• Familiarity with monitoring/logging stacks (Prometheus, ELK)
• Publications or contributions to open-source projects in data engineering or ML