Senior Member Technical Staff

Key Responsibilities

• Architect & Build Scalable Systems: Design, develop, and maintain robust data pipelines and microservices using Python.
• Intelligent Document Processing: Develop document parsing and extraction pipelines (for PDFs, HTML, structured/unstructured data) that can reliably handle millions of documents.
• AI/ML Integration: Implement and leverage technologies like LLMs (Large Language Models), Langgraph, AgenticAI and Prompt Engineering to create advanced, AI-driven solutions.
• Data Strategy: Leverage NoSQL (specifically DynamoDB) and other database technologies to optimize data storage and retrieval for high-scale applications.
• Stream Processing: Implement and manage real-time data streaming solutions using Kafka to support event-driven and responsive architectures.
• Cloud Infrastructure: Architect and deploy services within AWS, ensuring best practices in security, scalability, and cost-optimization.
• Code Excellence & Collaboration: Write clean, maintainable, and highly efficient code, lead rigorous code reviews, and actively collaborate with the team on system design and technical decision-making.
• System Optimization: Troubleshoot complex distributed systems issues and optimize performance across the entire tech stack.
• Mentorship: Act as a technical pillar for the team, mentoring junior engineers and fostering a culture of rapid experimentation and "failing fast" (and learning faster).
• Education: Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field.
• Experience: 7 to 10 years of professional software development experience, specifically within a product-based company where you have navigated rapid scaling.
• Core Tech: Expert-level proficiency in Python and Java.
• AI/Document Experience: Document parsing experience—extracting structured data from PDFs, HTML, XML, or other unstructured formats.
• Database Expertise: Deep hands-on experience with NoSQL databases (specifically DynamoDB) and a solid understanding of data modeling for performance.
• Messaging & Integration: Proven experience with Kafka or similar message brokers for building event-driven systems.
• Cloud Native: Strong experience with AWS services (Lambda, EC2, S3, RDS, etc.) and building cloud-native applications.
• Infrastructure as Code: Experience with K8s and advanced Infrastructure as Code tools such as CDK, Terraform, or CloudFormation.
• Data Engineering: Familiarity with building and maintaining Data Pipelines (ETL/ELT processes) and handling large-scale datasets.
• Architecture: Strong understanding of Microservices, RESTful APIs, and distributed systems design.
• Mindset & Communication: A proactive, "owner" mindset with the ability to thrive in an ambiguous, fast-paced environment, and excellent communication skills.

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