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    Job · 15004560

    NE

    Senior Data Engineer (AI/ML)

    neolatika · Pune · onsite · Posted 50d ago

    Not disclosed6–8 yrsfulltimeSourced fromFoundit

    About a minute. You'll make an account on the way through.

    About this role

    Key Responsibilities

    • Design, develop, and maintain scalable data pipelines for structured and unstructured data.
    • Build and optimize data lakes, data warehouses, and data platform architectures.
    • Support AI/ML model development by preparing, transforming, and delivering high-quality datasets.
    • Develop and manage ETL/ELT workflows using modern data engineering tools.
    • Collaborate with data scientists and ML engineers to operationalize machine learning models.
    • Implement data quality, validation, and monitoring frameworks.
    • Optimize data processing performance and cost across cloud platforms.
    • Develop and maintain data APIs and data services for downstream applications.
    • Ensure data security, governance, and compliance standards are met.
    • Mentor junior data engineers and contribute to best practices and architecture decisions.

    Required Skills & Qualifications

    • Bachelor's in computer science, Data Engineering, Information Systems, or related field.
    • 6+ years of experience in data engineering or data platform development.
    • Strong proficiency in Python, SQL, and distributed data processing frameworks.
    • Experience with big data technologies such as Spark, Hadoop, or similar platforms.
    • Hands-on experience with cloud platforms (AWS / Azure / GCP).
    • Experience building ETL pipelines and data orchestration workflows.
    • Understanding machine learning pipelines and model lifecycle.
    • Experience with data warehousing solutions such as Snowflake, BigQuery, Redshift, or similar.
    • Strong knowledge of data modeling, data governance, and data quality frameworks.

    Preferred Skills & Key Competencies

    • Experience with ML pipelines, feature stores, and model deployment frameworks.
    • Familiarity with MLOps tools such as MLflow or Kubeflow.
    • Experience with streaming technologies like Kafka or Spark Streaming.
    • Knowledge of containerization and orchestration (Docker, Kubernetes).
    • Experience working in Agile or DevOps environments.
    • Strong problem-solving and analytical skills.
    • Ability to work effectively with cross-functional teams.
    • Strong communication, documentation, and mentoring skills.

    Sourced from foundit

    Skills

    data orchestration workflowsData Quality Frameworksdistributed data processing frameworksPythonSQLData GovernanceETL PipelinesData Modeling

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