• Design, build, and deploy end-to-end AI pipelines for content moderation • Engage with operational business stakeholders to identify key initiatives • Extract text/tables from PDF documents using OCR (Tesseract, AWS Textract, Azure Form Recognizer) • Create/build labeling pipelines for training datasets • Train custom ML models (NLP/OCR) for document classification • Implement end-to-end pipeline: PDF → labeling → training → inference → API serving • Version control all code, datasets, and models using Git • Deploy models as REST APIs (FastAPI) • Work collaboratively to align business requirements with data and analytical solutions

• Architect and lead the development of ingestion, indexing, and retrieval systems across our archival infrastructure • Drive technical strategy for API performance, reliability, and scalability across high-throughput services • Design and optimize data models and query patterns for ClickHouse, Postgres, and RocksDB at scale • Lead system design discussions and make architectural decisions on caching, distributed data patterns, and performance trade-offs • Set standards for operational excellence, debugging methodologies, and performance profiling across production systems • Mentor senior engineers and elevate the team's technical capabilities • Partner with leadership on long-term technical roadmap and infrastructure investments • Identify and drive solutions to complex, ambiguous problems that span multiple systems

• Designing, optimizing, and maintaining Azure SQL database environments • Supporting development teams and modern DevOps practices • Partnering closely with engineering teams and architects to ensure database design, deployment, and performance are aligned with application development and agile delivery • Designing and modeling relational database solutions using SQL technologies (e.g., Azure SQL) • Deep involvement with engineering team in the Data Access Layer using EF CodeFirst modeling, mappings, and LINQ queries • Establishing data unit test patterns and framework for LINQ/query expressions and other data access patterns • Planning, scripting, reviewing, and rolling out EF migrations • Analyzing and optimizing SQL query performance • Optimizing performance of application-generated queries on Azure SQL Database • Operating as a subject matter expert on data access and ORM integration • Automating database deployment and maintenance tasks using CI/CD pipelines and infrastructure-as-code • Managing and synchronizing database changes across environments • Overseeing and managing Azure cloud-hosted databases • Integrating and maintaining monitoring and alerting solutions for proactive database performance management • Exploring and recommending new technologies, patterns, and methods • Collaborating with Architects to implement high availability and disaster recovery solutions • Participating in agile ceremonies and on-call rotations as needed

• Build scalable APIs and microservices that serve ML models in production with low latency and high availability. • Work with the product engineering teams to implement reliable integrations with LLM APIs (Claude, GPT, Gemini). • Implement monitoring, measurement, and observability systems for AI workflows to ensure reliability and performance. • Build the integration points between the AI/ML Platform stack and the core application, supporting product development and long term technological investment. • Support data pipeline development and maintenance that enable ML model training and inference.