AI Data Engineer (US)

EdgeQ Technologies • Python • SQL • ETL/ELT • 18 days ago
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Salary
$105k – $185k per year
Location
In office (Boston)
Seniority
Junior · 2+ years exp
Employment
Full-Time

First seen by Alion on Oct 8, 2026.

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EdgeQ Technologies

Founded in 2026, EdgeQ Technologies is a software development, staffing, and business services provider headquartered in Anantapur, Andhra Pradesh, India. The company delivers an integrated suite of technology and workforce solutions for startups and enterprises across domestic and international markets. Its core offerings span custom web and mobile application engineering, digital marketing, and business software products - including applicant tracking systems (ATS), custom CRMs, payroll, and billing platforms - alongside IT and non-IT recruitment, talent placement, and business process outsourcing (BPO) operations.
edgeqtechnologies.com • HQ: Anantapuram, India • 501-1000 employees • Est. 2026
Tech stack of EdgeQ Technologies

We are seeking a skilled AI Data Engineer to design, build, and optimize scalable data pipelines supporting AI/ML applications. The ideal candidate will have 2-5 years of experience and strong expertise in Python, SQL, and Apache Spark.

Key responsibilities

  • Design, develop, and maintain scalable data pipelines for AI/ML applications.

  • Build and optimize ETL/ELT workflows using Python, SQL, and Apache Spark.

  • Process and transform large datasets for machine learning and analytics.

  • Collaborate with data scientists and ML engineers to prepare high-quality training data.

  • Develop data validation, quality, and monitoring processes.

  • Optimize data pipelines for performance, reliability, and scalability.

Required qualifications

  • 2-5 years of professional experience in data engineering or a related field.

  • Strong programming skills in Python.

  • Advanced knowledge of SQL and relational databases.

  • Hands-on experience with Apache Spark and distributed data processing.

  • Understanding of AI/ML concepts and data workflows.

Nice to have

  • Experience with cloud platforms such as AWS, Azure, or Google Cloud.

  • Familiarity with data warehouses, lakehouse architectures, and streaming technologies like Kafka.

  • Knowledge of Docker, Kubernetes, and MLOps tools.

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