Engenious

AI Engineer

• Develop document-processing pipelines with ML/LLM-based extraction and agentic workflows
• Build and extend Python microservices for document ingestion, classification, extraction, validation, and post-processing
• Develop autonomous AI agents using tool calling, structured outputs, and advanced reasoning
• Prepare evaluation datasets and define ground-truth target values for extracted data
• Evaluate extraction results, analyze failures, and improve prompts, schemas, and pipeline logic
• Build automated testing and regression frameworks for prompts, models, agents, and extraction workflows
• Optimize AI systems for accuracy, latency, throughput, reliability, and cost
• Work with asynchronous processing infrastructure using AWS services such as ECS, SQS, SNS, RDS, and S3
• Use AI-assisted development tools such as Claude Code, Cursor, and similar solutions while following engineering, testing, and code-review best practices
• Prototype new AI solutions in collaboration with engineering teams

Artificial Intelligence / Machine Learning Engineer

Backend Engineer

• Build and ship end-to-end features across the high-load backend services
• Design scalable APIs and services
• Build and integrate AI-powered features into product workflows
• Work with LLM-based systems and AI-driven services
• Improve performance, reliability, and system architecture
• Collaborate with product, design, and AI teams (both Engenious and client-side)
• Contribute to technical decisions and engineering standards
• Mentor engineers and support team growth

Full-stack Engineer

• Drive the team's technical direction and keep day-to-day progress aligned with project goals.
• Work directly with stakeholders to clarify requirements, set and defend priorities, and communicate trade-offs and status clearly.
• Own the roadmap-to-execution translation: break down initiatives, sequence work, and keep the backlog focused.
• Oversee team execution, review priorities, monitor progress, and course-correct early when things drift.
• Deep-dive into technical processes when needed: code, architecture, pipeline behavior, and incident root cause.
• Make and document architectural decisions across the platform and the data processing services.
• Coach engineers, raise the technical bar, and ensure delivery quality without becoming a bottleneck.
• Drive effective use of AI coding assistants (e.g. Claude Code) across the team to accelerate delivery without sacrificing quality.

Artificial Intelligence / Machine Learning Engineer

Backend Engineer

Tech Lead