The VP of Engineering will lead the technical vision and execution of LakeFusion.AIs platform, leveraging cutting-edge technologies such as Large Language Models (LLMs), Vector Search, and Databricks. This role requires a deep understanding of scalable data platforms, modern AI/ML methodologies, and experience working within or alongside Databricks ecosystems to drive innovation in Master Data Management (MDM) and AI-powered solutions.

Requirements

Key Responsibilities:

Define and execute the engineering roadmap with a strong emphasis on Databricks capabilities, LLMs, and Vector Search technologies.
Architect scalable and high-performance data pipelines using Databricks, Delta Lake, and Unity Catalog, ensuring optimal data governance and analytics.
Lead the integration of LLMs and vector databases to enhance entity resolution, semantic search, and intelligent data enrichment.
Drive collaboration with Databricks tools and features to implement real-time data processing workflows and AI/ML solutions.
Oversee the design and development of AI-powered MDM solutions, ensuring compliance with business rules such as match and merge, survivorship, and anomaly detection.
Build and mentor a high-performing engineering team, fostering a culture of innovation and collaboration.
Collaborate with internal stakeholders, clients, and partners to deliver scalable solutions that align with business goals.

Qualifications:

Educational Background
:
- Master s or Ph.D. in Computer Science, Artificial Intelligence, Data Science, or related fields.
Experience
:
- 12+ years of engineering leadership experience in AI/ML or data platform-driven organizations.
- Proven expertise in deploying LLMs (e.g., GPT, BERT) and vector search technologies (e.g., Pinecone, Milvus, Weaviate).
- Hands-on experience with Databricks
  workflows, including Delta Lake, Unity Catalog, and Machine Learning pipelines. Candidates with direct exposure to working in or with Databricks teams are highly preferred.
- Strong background in MDM processes and data engineering best practices.
- Demonstrated success in building scalable, cloud-native architectures on AWS or Azure.
Technical Skills
:
- Advanced knowledge of AI/ML frameworks such as PyTorch, TensorFlow, and Hugging Face.
- Proficiency in Databricks features, including Auto Loader, SQL Analytics, and real-time processing pipelines.
- Expertise in vector database solutions and tools for building LLM-based applications.
- Familiarity with compliance standards like HIPAA and GDPR.
Soft Skills
:
- Strategic thinker with the ability to align engineering initiatives with broader business objectives.
- Strong leadership and communication skills, capable of engaging with clients, partners, and stakeholders.

Preferred Qualifications:

Direct experience working within Databricks or significant collaboration with their ecosystem.
Industry expertise in healthcare or life sciences, retail etc.
Familiarity with Databricks Machine Learning and tools for scaling AI/ML models.

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VP of Engineering