We turn the data that monitoring networks already capture into decisions.
EsolverIntegral SAS is a team built project by project, with professionals who prefer the complexity of a real problem over the comfort of an instruction.
How do we work?
We connect to the data of already-installed monitoring networks, centralize it, clean it, and structure it into a reliable time-series database, and on those time series we apply analytics and models that reveal patterns, detect anomalies, and anticipate behaviors. We work on the data; the installation of sensors and the integration of the Internet of Things belong to our partners, and we contribute the analytics layer that completes what they build.
Every project begins with questions about the business, rather than with a technological solution. Before writing a line of code or training a model, we devote the necessary time to understanding what the client wants, what data they have, what we can do, and what we choose to do. That initial rigor determines the real usefulness of the solution and its effective use.
Every solution integrates data governance policies, information security strategies, and quality criteria as a constitutive part of the project from day one. We take on the challenge directly, implement the solutions, and follow up on how they perform. That scope demands the highest level of commitment and rigor, proper to tangible and measurable results.
What we look for in the people who join
We look for professionals with the curiosity and motivation to read a business problem, identify the existing monitoring data and the missing data, propose a solution architecture suited to the available resources, and take it to production with rigorous technical standards. The ability to communicate technical findings to business counterparts carries the same weight as mastery of the tools.
Profiles we work with
Each profile contributes a piece of the path that goes from raw monitoring data to the decision in production. These are the profiles we build with.
Data analyst
Advanced command of SQL and experience with visualization tools such as Power BI, Tableau, or Looker. Ability to identify patterns and anomalies in time series from monitoring networks, and translate them into management indicators and actionable alerts. Familiarity with exploratory-analysis notebooks and modern data platforms such as Databricks or Google BigQuery.
Data engineer
Design and construction of batch and streaming processing pipelines with tools such as Apache Airflow, dbt, or Apache Spark, to ingest, clean, and centralize monitoring data into time-series databases. Experience with time-series databases such as TimescaleDB, with cloud platforms, and with modern architectures such as the Data Lakehouse. Knowledge of columnar formats such as Parquet or Delta Lake and of ingestion frameworks such as Apache Kafka or the MQTT protocol.
Data scientist
Development of time-series models for anomaly detection, failure prediction, and scenario projection, with Python and libraries such as Scikit-learn, TensorFlow, or PyTorch. Judgment to select and validate the methodological approach according to the available data and the client's constraints. Experience with MLflow or similar tools for experiment tracking and versioning.
MLOps engineer
Management of the full lifecycle of models in production, with versioning, data-drift and model-drift monitoring, and automated retraining, over the continuous flow of monitoring data. Experience with platforms such as MLflow, Kubeflow, BentoML, or AWS SageMaker for deployment and experiment traceability. Knowledge of infrastructure as code and of continuous integration and delivery practices applied to machine learning pipelines.
Software developer
Construction of REST and GraphQL programming interfaces to connect the client's monitoring networks, integrate the models into their systems, and expose the results. Experience with frameworks such as FastAPI or Node.js, containers with Docker, and orchestration with Kubernetes. Application of good continuous integration and delivery practices, automated testing, and technical documentation oriented to maintainability.
Software architect
Design of distributed and scalable systems for time-series processing, with patterns such as microservices, event-driven architectures, and hexagonal design. Experience in long-term decisions that balance technical soundness, flexibility, and total cost of ownership. Knowledge of serverless platforms and of infrastructure-as-code tools such as Terraform or Pulumi.
Dashboard and visualization developer
Construction of real-time monitoring dashboards, management indicators, and reports that turn the time series into a decision. Command of visualization libraries such as D3.js, Plotly, or Recharts, and of frontend frameworks such as React. Judgment to present the alert and the indicator with clarity for the operator and for the business owner.
Compliance and documentary traceability specialist
Design of continuous monitoring and documentary traceability solutions for regulated environments. Knowledge of regulatory-limit verification, auditable event logs, and the generation of regulatory reports, such as the environmental report for Colombia. Familiarity with the operation of laboratories accredited under the NTC-ISO/IEC 17025:2017 standard and with environmental management systems.
Information security and data governance specialist
Implementation of frameworks based on the ISO/IEC 27001:2022 standard or the NIST Cybersecurity Framework 2.0, with access control, encryption, and incident management policies over monitoring data. Design of governance models over the ownership, quality, and lifecycle of the data. Experience with tools such as Apache Atlas, Collibra, or Microsoft Purview for the cataloging and traceability of data assets.
What we offer in return
We offer a way of working oriented to measurable results agreed on from the start, with full technical autonomy within the stated objective and freedom in execution timing. Financial compensation is defined per project, and is complemented with additional incentives for meeting deliveries, adherence to the agreed quality standards, and satisfaction of the commitments made with the client.
Technical autonomy
Each team member contributes from their knowledge and experience with full freedom of judgment, in accordance with the stated objective and the commitments made with the client.
Agreed results
Each project defines its deliverables, its quality standards, and its timelines from the start. The work is measured by the results achieved.
Compensation and incentives
Compensation is agreed per project, and includes financial incentives for meeting deliveries, quality, and adherence to the commitments made with the client.
Do you recognize yourself in this?
We are a team under construction, with real projects, complex problems, and a way of working that places knowledge and technical judgment above hierarchy and routine. If what you find here reflects your way of understanding work and technology, there is a place for you at EsolverIntegral SAS.
Write to us at any time, with or without a published vacancy; we want to meet people who share this way of working even before the project arrives.
Write to us
Tell us: what area do you work in? What kind of problems interest you to solve? And why do you think you would fit into this way of working? Write to us in free form, with a single requirement: that it be genuine.
Write to usBy sending us your résumé or any personal information, you authorize EsolverIntegral SAS to process your data for the exclusive purposes of selection, evaluation, and professional contact, in accordance with the Personal data processing policy, which complies with Ley 1581 de 2012 (Law 1581 of 2012) and Decreto 1377 de 2013 (Decree 1377 of 2013).
EsolverIntegral SAS is a company in a growth stage. Projects set the pace of hiring, and transparency about that process is part of the way we work.