Department: Artificial Intelligence

    Data Engineer – AI, Big Data & MLOps Platforms

    Chandigarh
    5+ years Experience

    We are seeking a highly experienced Data Engineer to build and operate the data, big-data infrastructure, and MLOps backbone for enterprise-scale AI/ML solutions. This role focuses on designing scalable data pipelines, cloud-native and hybrid infrastructure, and production-grade AI/ML deployments, while ensuring security, reliability, and performance.

    The ideal candidate has strong hands-on experience with big-data platforms, PySpark, MLOps workflows, and cloud infrastructure, and works closely with data scientists to turn data into actionable insights.

    Key Responsibilities

    Data Engineering & Big Data Pipelines

    • Architect, build, and maintain scalable, fault-tolerant data pipelines across cloud and on-prem environments.
    • Own the end-to-end data lifecycle: ingestion, preprocessing, transformation, validation, enrichment, and feature engineering.
    • Develop large-scale data processing pipelines using PySpark / Apache Spark for structured and unstructured data.
    • Enable seamless data flow across data lakes, warehouses, streaming platforms, and AI/ML systems.
    • Debug, tune, and optimize distributed pipelines for performance, reliability, and cost efficiency.

    Scalable Infrastructure & Cloud Architecture

    • Design and operate scalable big-data and AI/ML infrastructure supporting batch and streaming workloads.
    • Build cloud-native and hybrid architectures using major CSPs (AWS, Azure, GCP).
    • Select and right-size compute (CPU/GPU), storage, and networking for data processing and ML workloads.
    • Configure and manage development, test, and production environments with high availability and elasticity.
    • Use Docker and Kubernetes to deploy and manage data and AI workloads.

    MLOps & AI Enablement

    • Design and contribute to MLOps pipelines covering data/versioning, feature pipelines, model training, deployment, and monitoring.
    • Enable reproducibility, automation, and scalability across the ML lifecycle.
    • Support infrastructure for model training and inference (CPU/GPU).
    • Implement monitoring for data quality, data drift, model performance, and pipeline health.

    Analytics & Insight Enablement

    • Partner with data scientists and analytics teams to deliver analytics-ready datasets and features.
    • Support large-scale data profiling and exploratory analysis to drive business and AI insights.

    Security, Governance & Reliability

    • Design and implement secure data and AI platforms, including IAM, encryption, network security, and secrets management.
    • Ensure compliance with enterprise data governance, privacy, and security standards.
    • Implement logging, monitoring, alerting, and incident response for data and MLOps pipelines.
    • Apply CI/CD and Infrastructure-as-Code (IaC) practices for consistent, auditable deployments.

    Required Skills & Qualifications

    • Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.

    • Strong experience as a Data Engineer in AI, big-data, or advanced analytics environments.

    • Hands-on expertise with PySpark / Apache Spark and distributed data processing systems.

    • Experience building scalable big-data infrastructure and pipelines.

    • Deep understanding of MLOps principles and lifecycle management.

    • Proficiency in Python and SQL (Scala/Java a plus).

    • Experience with big-data and streaming frameworks (Spark, Kafka, Airflow, Flink, Hadoop ecosystem).

    • Strong knowledge of cloud-native architectures, hybrid environments, and infrastructure fundamentals (compute, storage, networking).

    • Proven ability to debug and optimize complex distributed systems.

    Preferred / Nice-to-Have Skills

    • • Experience deploying production AI/ML solutions on cloud platforms.
    • • Familiarity with feature stores, model registries, lakehouse architectures, and modern data warehouses.
    • • Knowledge of GPU workloads and optimization.
    • • DevOps / Platform Engineering experience.
    • • Cloud or Kubernetes certifications (AWS, Azure, GCP, CKA).

    What We Offer

    Opportunity to build enterprise-scale AI, big-data, and MLOps platforms
    Ownership of scalable infrastructure and data pipelines
    Collaboration with AI, ML, and analytics teams to drive real business impact
    Competitive compensation and benefits

    Send your resume to

    hr@jupitice.com