Vector DB Engineer

TECHNICAL KEY SKILLS:

• Seeking skilled Vector db Engineer to design, develop, and maintain scalable and efficient vector database systems. The ideal candidate will have expertise in vector databases, machine learning, and distributed systems.
• Key Responsibilities:
• 1. Design and develop vector database architectures to support large-scale AI and ML applications.
• 2. Implement efficient indexing and querying algorithms for vector data. Optimize database performance for high-dimensional vector data.
• 3. Collaborate with data scientists and engineers to integrate vector databases with AI/ML pipelines. Ensure data security, scalability, and reliability.
• 4. Embedding data as vectors for AI systems to interact more meaningfully. Embedded data as vectors stored efficiently while preserving complexities for systematic comparison of unique data to find similarities.
• Requirements:
• 1. Experience with vector db (e.g., Pinecone, Weaviate, Faiss, Cloud Sql for mySql, PostgreSql).
• 2. Strong background in computer science, mathematics, or related fields. Proficiency in programming languages (e.g., Python, C++, Java).Knowledge of machine learning and deep learning concepts. Experience with distributed systems and cloud computing.
• Experience with containerization (e.g., Docker) and orchestration (e.g., Kubernetes).Familiarity with cloud-based vector databases (e.g., AWS, GCP, Azure).
• Strong problem-solving skills and attention to detail.
• Opportunities for professional growth and development..Collaborative and dynamic work environment

Responsibility of / Expectations from the Role

1. Design and develop vector database architectures to support large-scale AI and ML applications.

2. Implement efficient indexing and querying algorithms for vector data. Optimize database performance for high-dimensional vector data.

3. Collaborate with data scientists and engineers to integrate vector databases with AI/ML pipelines. Ensure data security, scalability, and reliability.

4. Embedding data as vectors for AI systems to interact more meaningfully. Embedded data as vectors stored efficiently while preserving complexities for systematic comparison of unique data to find similarities