[Hiring] Machine Learning Data Engineer @EdgeQ Technologies Private Limited

Role Description

We are seeking a skilled and experienced Machine Learning Data Engineer to join our growing AI and Data Engineering team in the United States. The ideal candidate will be responsible for designing, developing, and maintaining scalable data pipelines, data infrastructure, and data processing systems that support machine learning and artificial intelligence applications.

The candidate should have strong expertise in Python, SQL, ETL/ELT pipelines, cloud data platforms, big data technologies, and machine learning data workflows. This position offers a fully remote work opportunity for qualified candidates across the United States.

Key Responsibilities

• Design, develop, and maintain scalable data pipelines for machine learning and AI applications.
• Build and optimize ETL/ELT workflows for structured and unstructured datasets.
• Develop data ingestion, transformation, validation, and processing solutions.
• Collaborate with Data Scientists, ML Engineers, and AI teams to prepare high-quality training datasets.
• Implement data preprocessing, feature engineering, and feature extraction pipelines.
• Manage large-scale datasets using distributed processing technologies.
• Develop and maintain cloud-based data infrastructure.
• Implement data quality checks, monitoring, and automated validation processes.
• Optimize data storage, retrieval, and processing performance.
• Support machine learning model training, evaluation, and deployment workflows.
• Integrate data from APIs, databases, streaming platforms, and external sources.
• Implement data governance, security, and compliance best practices.
• Maintain technical documentation and participate in code reviews.

Qualifications

• 3–8 years of professional experience in Data Engineering, Machine Learning Data Engineering, or related roles.
• Strong programming skills in Python and SQL.
• Hands-on experience building ETL/ELT pipelines.
• Experience with Apache Spark, PySpark, or similar distributed processing frameworks.
• Strong knowledge of relational and NoSQL databases.
• Experience with AWS, Microsoft Azure, or Google Cloud Platform.
• Familiarity with data warehouses such as Snowflake, BigQuery, Redshift, or Databricks.
• Experience with Apache Airflow, Dagster, or similar workflow orchestration tools.
• Understanding of machine learning data preparation and feature engineering.
• Knowledge of data modeling, data lakes, and lakehouse architectures.
• Experience with Git, CI/CD, and automated testing.
• Strong analytical, debugging, and problem-solving skills.

Preferred Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Information Technology, Engineering, or a related field.
• Experience with MLflow, Kubeflow, Feast, or other MLOps and feature store technologies.
• Familiarity with TensorFlow, PyTorch, or Scikit-learn.
• Experience with Apache Kafka or real-time streaming pipelines.
• Knowledge of Delta Lake, Apache Iceberg, or Apache Hudi.
• Experience supporting Generative AI and LLM data pipelines.
• Familiarity with vector databases such as Pinecone, Weaviate, or Milvus.
• Knowledge of Docker, Kubernetes, and infrastructure automation.
• Experience with data governance, lineage, and privacy frameworks.

Salary & Benefits

• Annual Salary: $110,000 – $175,000 USD (Indicative range based on experience and technical expertise).
• Competitive compensation and performance-based incentives, where applicable.
• 100% remote work opportunity.
• Flexible working arrangements.
• Health, dental, and vision insurance.
• Paid time off and holidays.
• Retirement savings benefits, where applicable.
• Professional development and technical certification support.
• Career growth and advancement opportunities.

Work Location

• United States – Fully Remote.
• Candidates from eligible locations across the United States are encouraged to apply.
• Applicants must be legally authorized to work in the United States.
• Ability to collaborate with distributed teams across different US time zones.
• Occasional virtual meetings and collaboration with engineering stakeholders may be required.

How to Apply

Interested candidates can submit their updated resume along with contact details, relevant Machine Learning Data Engineering experience, technical skills, and links to GitHub repositories or project portfolios.

Equal Opportunity Employer

We welcome qualified candidates from diverse professional backgrounds and encourage applications from eligible candidates across the United States.