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Monks Technology Services, part of Media.Monks and S4 Capital, is seeking a Senior Full-Stack Engineer to own end-to-end features for an in-house platform handling test data ingestion, transformation, and AI-assisted analytics.
You'll work with Python backend services, React/TypeScript interfaces, and an AI assistant, collaborating with test engineers and data owners while maintaining production-grade quality and observability.
Please note that we will never request payment or bank account information at any stage of the recruitment process. As we continue to grow our teams, we urge you to be cautious of fraudulent job postings or recruitment activities that misuse our company name and information. Please protect your personal information during any recruitment process. While Monks may contact potential candidates via LinkedIn, all applications must be submitted through our official website (monks.com/careers).
Location: Remote from Colombia
.Monks Technology Services, part of Media.Monks and S4 Capital, is a global consulting firm mastering AI-powered transformations for the Fortune 100. We combine long-term strategic thinking, deep enterprise experience, and a human-centered approach to help clients transform business processes and dominate their industries.
As a Senior Full-Stack Engineer, you'll build an in-house platform that helps battery engineers ingest, explore, and act on test data. The platform transforms raw equipment output into a consistent data model, supports qualification decisions through interactive charts and dashboards, and provides an AI assistant grounded in the same data. You'll own features end to end from the first stakeholder conversation through data models, APIs, and user interfaces, with substantial autonomy. As the platform replaces tools engineers rely on daily, you'll help earn their trust through accuracy, traceability, and a thoughtful rollout.
Design and build Python backend services, REST APIs, data access layers, and background jobs for ingestion and long-running computations.
Develop robust parsers that transform vendor files, CSVs, and binary exports into validated records, handling inconsistent headers, changing units, and malformed inputs with clear rejection reasons.
Implement traceable data transformations for capacity, efficiency, resistance, and cycle aggregates, ensuring consistent results across pipelines and on-demand calculations.
Build React and TypeScript interfaces with interactive charts for large time-series datasets, including millions of points, cross-cell and cross-test overlays, and detail-preserving zoom.
Integrate an AI assistant using LLM APIs, tool calling, and retrieval over platform data and documentation, with citations and evaluations that identify incorrect answers.
Work directly with test engineers, lab supervisors, and upstream data owners to clarify needs, scope deliverables, and explain technical trade-offs.
Own technical decisions across the data model, API, and user experience, delivering features from discovery through production with minimal oversight.
Implement production observability using structured logging and request correlation, and support reliable delivery through Docker and GitLab CI.
Maintain rigorous tests, strict typing, small reviewable changes, and accurate documentation while avoiding premature abstraction.
Support an incremental transition from existing tools without disrupting engineers’ daily workflows.
Other duties as assigned
6+ years of experience building production software, with substantial experience across both backend and frontend development.
Strong Python backend skills, including REST API design, asynchronous services, background processing, authentication, authorization, and performance tuning under real load.
Experience with relational data modeling, fluent SQL, and schema migrations in live systems.
Hands-on Python data processing experience with pandas, Polars, or NumPy.
Strong modern TypeScript and React skills.
Experience shipping interactive data visualizations to real users, ideally with large or time-series datasets.
Experience building parsers and transformation pipelines for messy real-world data, including vendor formats, schema drift, partial files, and unit handling.
At least one LLM integration shipped in a product, including prompt design, tool or function calling, retrieval, and a method for evaluating output quality.
A track record of leading requirements conversations with non-engineers and translating them into scoped deliverables, supported by concrete examples.
Demonstrated autonomy through projects you have scoped, built, and shipped with minimal oversight.
Strong communication skills and English proficiency at B2 level or above.
Experience with FastAPI, Pydantic v2, SQLAlchemy 2.x, and asynchronous Python at scale.
Familiarity with a Python 3.12 toolchain using pytest