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Senior Machine Learning Engineer

Xebia

6 - 12 years

Bengaluru

Posted: 09/05/2026

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Job Description

Job Title : Senior Machine Learning Engineer

Job Location : Bengaluru

Exp : 6-12 years


Position Overview : We are looking for a Machine learning Engineers with strong expertise on ML /AI . Should have good experience working on ML models developments and deploying the ML platforms


Roles & Responsibilities :


  • Design, build, and optimize scalable batch and streaming data pipelines using distributed framework like Spark/Databricks/Kafka.
  • Design & Code - robust data models, feature pipelines, and ETL/ELT frameworks for analytics and ML.
  • Ensure data quality, observability, lineage, and performance across data platforms.
  • Build and refine ML models endtoend: feature engineering, training, evaluation, and deployment.
  • Partner with data scientists to convert prototypes into productiongrade ML solutions.
  • Implement CI/CD, model versioning, monitoring, and automation across data and ML workflows.
  • Product Driven Mindset: Collaborate with engineering, product teams to deliver datadriven outcomes.


Skill sets Required :


  • 6+ years of experience in ML-Data Engineering development.
  • Strong SQ/NoSQL, Python, PySpark, and ML Models Lifecycle & Frameworks (MlFlow, Spark-ml), Orchestration (Airflow/Oozie/Dagster etc)
  • Expertise in Big Data modeling, Distributed processing, and Lake & Warehouse architectures at large operational scale.
  • Handson with ML lifecycle tools (MLflow, Feature Store, model monitoring, Evaluation).
  • Strong Analytical & Problem Solving Skills - Data/Process Intensive Design/Architecture, Strong debugging, optimization.
  • Basic hold on foundational modelling concepts & algorithms such as - Regression, Classification and Statistical models.
  • Good Hold on Concepts- Distributed File Formats, Open table Formats, Distributed transaction management, Workload Parallelizing.
  • Jands on - Unix, Hadoop, Object store fundamental operations & commands
  • Basic skilled with containerized processing (Docker + K8s)

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