AI Data Engineer

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At Quantum Gandiva AI, we're not just another startup. We are a deep-research AI lab based in Visakhapatnam, and we're building the Autonomy Stack for self-improving AI. We believe in practical, reproducible research, and we're looking for a foundational team member to build with us.

This isn't your typical "entry-level" role. We're looking for a passionate AI Data Engineer to architect our data pipelines from scratch to production. Years of corporate experience don't matter to us. Your GitHub, your personal projects, and your passion for building fast, reliable, and secure systems are what we care about.

Why You'll Thrive Here:

Build from Zero:
This is a greenfield project. You will own the entire data lifecycle.

Work with the Best Tools:
You'll be using Python, pgvector, Airflow, Docker, and the latest models from Hugging Face and LangChain.

Direct Impact:
Your work is not a feature; it is the foundation of our entire research effort into AGI and Quantum AI.

Are you the builder we're looking for? If you're obsessed with data and have a portfolio of AI projects that proves it, we want to talk to you.

See the full, detailed job description below

Job Description: Full-Time AI Data Engineer (Entry-Level/Intern Focus)

*Position Title:*
AI Data Engineer (Full-Time, Entry-Level Opportunity)

*Location:*
Visakhapatnam, Andhra Pradesh (Remote/hybrid possible, local preferred)

*Start Date:*
September 2025 (flexible for the right candidate)

*Role Overview:*

We are seeking an enthusiastic AI Data Engineer for a full-time role to design, build, and optimize scalable data pipelines from scratch to production level, ensuring efficient and secure data flow for AI systems. This position offers hands-on experience and mentorship, ideal for those eager to grow in AI data engineering.

*Key Responsibilities:*

• Develop ETL pipelines to ingest, transform, and load data from APIs, databases, and files, managing structured and unstructured formats.

• Clean, normalize, and deduplicate data with validation checks to maintain high quality and consistency.

• Design and manage databases (e.g., PostgreSQL, MongoDB, Redis), including vector databases like pgvector for advanced AI search capabilities.

• Monitor pipeline performance, implement error handling, rate limiting, and cost optimization for reliable systems.

• Enforce data governance, security (e.g., encryption, access controls), and compliance throughout the development lifecycle.

• Collaborate on integrations with AI/ML components, preparing data for model use and enabling real-time processing.

• Troubleshoot and iterate to build fast, reliable, and secure implementations.

*Required Skills and Qualifications:*

• Strong proficiency in Python for ETL (e.g., Pandas, Polars) and scripting; familiarity with SQL for complex queries.

• Experience with database management (relational/NoSQL) and orchestration tools like Apache Airflow.

• Knowledge of data quality, deduplication, schema testing, and real-time streaming (e.g., Kafka).

• Understanding of cloud platforms (e.g., AWS, GCP) for storage and computing.

• Capability to build fast, reliable, and secure systems, including infrastructure as code (e.g., Terraform) and containerization (e.g., Docker).

• Proficiency with AI coding tools like Claude for code generation and Cursor AI editor for rapid development.

• Well-versed in latest models and modules (e.g., Hugging Face for embeddings, LangChain for integrations).

• Add-on skill: Context engineering for optimizing data flows in AI contexts.

*Experience Requirements:*

Years of professional experience are not mandatory; however, candidates must demonstrate a portfolio of self-projects where they have built AI agents from scratch, showcasing use of latest models and tools to create fast, reliable, and secure systems.