Machine Learning Engineer

Role: Machine Learning Engineer (MLE) with GCP - Must Have

Experience: 5 to 9 Years

Location: Bangalore

Notice Period: Immediate (OR) Serving Notice 10 Days Less Only

Interview Mode: Face to face on (10-Sep)

Job Description

We are seeking a skilled Machine Learning Engineer to design, develop, support and maintain critical MLE workflows that generate large-scale recommendations. These workflows are orchestrated through a custom in-house scheduling tool and Python, Bash, and Spark on a YARN-managed on-premise cluster or on GCP. The engineer will be responsible for ensuring development of scalable/reliable solution, deployment, monitoring, and troubleshooting jobs to minimize downtime and maintain system reliability. The role also involves implementing observability metrics and monitoring solutions, using tools such as OTEL, Kibana, REST APIs, and custom instrumentation. The ideal candidate will be comfortable collaborating via GitHub (PRs), proactive in identifying improvement opportunities, and effective at development and production monitoring and support.

Required Skills

• Proficiency with Python (PySpark), Bash, Kotlin or JVM based language
• Experience with Apache Spark on YARN-managed clusters (large-scale, on-premise or GCP like environments preferred) in distributed environment
• Familiarity with workflow orchestration tools (Airflow, Luigi, or custom equivalents)
• Knowledge of observability/monitoring practices using OTEL, Kibana, REST APIs, and custom metrics instrumentation
• Hands-on experience with GitHub workflows (pull requests, branching strategies, code reviews)
• Experience in API development and Kafka or like messaging technologies preferred.

Soft Skills

• Strong analytical and troubleshooting skills with attention to detail
• Clear and effective communication, especially in cross-functional environments
• Proactive mindset with a focus on reliability and continuous improvement
• Collaborative attitude, able to work effectively with developers, data scientists, and operations staff.