Overview

Job Role: Data Science & AI Engineer - Big Data Specialization

Responsibilities

• End-to-End AI/ML Engineering: Design, develop, and deploy scalable predictive models, statistical frameworks, and machine learning pipelines optimized for high-volume enterprise production.
• Big Data Pipeline Design: Build and maintain fault-tolerant, high-throughput data ingestion and ETL/ELT pipelines using distributed technologies (Spark, Hadoop, Kafka) and cloud warehouses.
• Production Deployment & MLOps: Integrate models into cloud-native architectures (GCP, AWS, or Azure), setting up continuous training, monitoring, and validation frameworks to ensure long-term stability and reliability.
• Exploratory Data Analysis: Mine complex, multi-terabyte datasets to uncover trends, anomalies, and actionable drivers using distributed query engines and statistical analysis.
• Rigorous Validation & Experimentation: Implement systematic hyperparameter tuning, cross-validation, and A/B testing protocols to optimize models for operational accuracy and business impact.
• Cross-Functional Collaboration: Partner with product managers, business stakeholders, and data engineers to translate ambiguous commercial problems into concrete technical roadmaps.
• Mentorship & Technical Standards: Guide and mentor junior engineers, championing software engineering best practices, modular code architecture, version control, and agile delivery.

Qualifications

• Programming & Querying: Advanced proficiency in Python and SQL. Working knowledge of Scala or Java is a plus.
• AI/ML Frameworks: Deep experience with Scikit-learn, TensorFlow, or PyTorch, coupled with distributed ML frameworks (such as Spark MLlib).
• Big Data Technologies: Demonstrated hands-on experience with distributed data processing systems, including Apache Spark, Hadoop ecosystem, Hive, and Apache Kafka.
• Cloud Computing: Strong working knowledge of cloud platforms (Google Cloud Platform preferred, or AWS/Azure) with emphasis on managed Big Data and AI/ML services (e.g., Dataproc, EMR, BigQuery, Vertex AI).
• Data Architecture: Solid understanding of distributed storage, schema modeling, data warehousing fundamentals, and large-scale ETL pipeline design.
• Software Engineering Fundamentals: Proficiency with Git, CI/CD workflows, unit testing, containerization basics, and agile development methodologies.
• Data Visualization & Communication: Ability to translate complex statistical and architectural concepts into clear narratives for executive and non-technical stakeholders via tools such as Tableau or Power BI.

Nice to have

• Domain Background: Prior experience delivering data science solutions within Mobile, Consumer Electronics, Fast-Moving Consumer Goods (FMCG), or Retail/Apparel industries.
• Specialized ML Domains: Applied exposure to Natural Language Processing (NLP), Large Language Models (LLMs), or Computer Vision workflows at scale.
• MLOps Proficiency: Experience with orchestration and tracking tools (such as Kubeflow, MLflow, Airflow, or Docker).
• Open Source & Community: Active open-source contributions or a public portfolio (e.g., GitHub, technical publications) showcasing distributed computing and AI implementations.

Education

• Certifications (Bonus): Cloud or Big Data credentials (e.g., Google Cloud Professional Data Engineer/Machine Learning Engineer, AWS Certified Machine Learning Specialty, or Databricks Certified Machine Learning Associate/Professional).