Architect and develop robust, end-to-end machine learning solutions, managing the complete ML lifecycle from development to production.
Collaborate closely with data engineers and data scientists to create highly scalable solutions and integrate them across the software development life cycle.
Evaluate cloud solutions for performance and cost-efficiency, and communicate technical concepts clearly to stakeholders and teams.
Lead the design and implementation of data science initiatives, defining methodologies and best practices.
Develop, evaluate, and optimize machine learning models to solve complex business problems.
Own the complete data science lifecycle from data exploration to deployment and monitoring.
Coderio designs and delivers scalable digital solutions for global companies. We combine strong technical expertise with a product mindset to lead complex software initiatives end-to-end, working with international clients and building long-term partnerships through technical excellence.
Develop, deploy, and maintain machine learning models in production environments using AWS SageMaker or similar platforms.
Perform exploratory data analysis, feature engineering, and build data pipelines to support scalable ML workflows.
Monitor production models, address performance issues, and collaborate with engineering and business stakeholders to deliver data-driven solutions.
Google is a global technology company that develops products and services to organize information and make it universally accessible and useful. The company is a large multinational with a culture focused on innovation, collaboration, and data-driven decision-making.
Design, build, and optimize data workflows for Machine Learning and GenAI solutions in cloud environments.
Develop and deploy Machine Learning and Generative AI models using AWS SageMaker.
Create and manage data and model pipelines to improve the efficiency of AI and machine learning systems.
Netrix Global provides the people, processes, and technology to run and scale modern data-driven businesses. It is a top system integrator with a culture focused on ownership, teamwork, and respect.
Build end-to-end Machine Learning pipelines, including data preparation, code optimization, automated model training, and prediction workflows.
Provide technical leadership in MLOps processes, establishing best practices for development, deployment, and maintenance.
Design and implement cloud-based architectures for AI and data solutions using platforms such as GCP, AWS, or Azure.
A technology company specializing in data-driven solutions for consumers, leveraging AI and cloud technologies to transform data into insights. The company fosters a dynamic, innovation-driven culture with a remote-first approach, collaborating across multidisciplinary teams.
Develop, deploy, and maintain production-grade Machine Learning models in cloud environments.
Build, maintain, and optimize data pipelines and monitor model performance post-deployment.
Partner with engineering, data, and business stakeholders to translate goals into scalable ML solutions.
In All Media is a global technology and design firm building impactful digital solutions through remote, distributed teams across LATAM. They partner with international clients across industries, providing long-term technical expertise and team augmentation.
Own end-to-end delivery quality for major engagements, translating ambiguous client needs into practical execution plans.
Lead solution architecture and technical decision-making, making pragmatic tradeoffs between speed, quality, and client value.
Build and ship production AI/ML systems using Python, ML frameworks, and cloud-native infrastructure while mentoring other engineers.
Eliza is a technology services company and Advanced-tier OpenAI partner that helps organizations build and deploy AI solutions, from generative AI to predictive analytics. They are a collaborative, mission-driven team focused on real-world AI impact.
Design, develop, and deploy foundational AI and ML models, building robust, scalable pipelines for advanced analytics.
Champion top-tier coding, testing, and MLOps practices, navigating ambiguity to refine pipelines and elevate workflows.
Partner with cross-functional stakeholders to convert strategic needs into technical specs and embed ML features into live applications.
Wave helps small businesses thrive so the heart of our communities beats stronger. They create an environment buzzing with creative energy and inspiration, valuing boldness, quick learning, and generous knowledge sharing.
Design, build, deploy, and maintain scalable machine learning and AI systems in GCP to solve complex business problems and create new value.
Partner across teams to understand problem definitions, data needs, and solution approaches, and support model deployment with MLOps practices.
Take ownership of production issues, perform root cause analysis, and improve documentation and quality assurance processes for ML systems.
General Mills makes food the world loves, operating across 100+ markets. As a large company, it prioritizes being a force for good and fosters a culture of bold thinking and big hearts, where employees challenge each other and grow together.
Design, build, deploy, and optimize machine learning models that process large volumes of complex, unstructured data.
Develop and maintain scalable ML pipelines capable of supporting millions of documents and diverse customer requirements.
Lead technical initiatives from early experimentation through production implementation and ongoing improvement.
The company develops intelligent automation solutions using AI and machine learning for large-scale document processing. It offers a highly autonomous engineering environment with a focus on continuous improvement and innovation.
Design, build, and operate scalable backend services, APIs, and data pipelines for ML-driven personalization.
Improve reliability, performance, and observability of production ML systems, including model versioning and safe rollout.
Collaborate with data scientists and product engineers to translate business needs into robust technical solutions.
Hungryroot uses AI to build a consumer-centric food and wellness company, acting as a personal assistant for healthy living by recommending and delivering healthy groceries, recipes, and supplements. They are a distributed team across 28+ US states with a remote-first culture emphasizing collaboration, flexibility, and an annual company retreat.
Build AI-powered tools and copilots across the SDLC to reduce cognitive load and eliminate manual steps.
Research and deploy GenAI solutions to improve delivery pipelines and system reliability.
Collaborate with Platform, SRE, and DevOps teams to integrate intelligent automation into the core engineering platform.
Coderio designs and delivers scalable digital solutions for global companies. They combine strong technical expertise with a product mindset and value autonomy and clear communication.
Develop, train, and evaluate machine learning models to support product objectives.
Prepare, clean, and analyze datasets for model training and validation.
Deploy, monitor, and improve machine learning models in production environments.
The partner company develops AI-powered products to solve real-world challenges. They offer a fully remote, flexible work environment with a focus on innovation and collaboration.
Design, build, and maintain scalable data pipelines for ingestion and transformation.
Work with Python, SQL, Apache Airflow, and Google Cloud Platform.
Collaborate with cross-functional teams to deliver reliable and high-quality data solutions.
Our partner is a technology company focused on AI and data solutions. The team values collaboration, innovation, and continuous learning, and offers a remote work environment.
Design and implement end-to-end document intelligence pipelines on AWS, including ML model development for document classification and field extraction.
Build scalable data processing systems handling PDFs up to 2000 pages, and own features from research through production deployment and monitoring.
Collaborate with subject matter experts to refine requirements, establish evaluation frameworks, and ensure extraction accuracy.
Exadel is an AI-first global tech company with 25+ years of engineering leadership, 2,000+ team members, and 500+ active projects powering Fortune 500 clients including HBO, Microsoft, Google, and Starbucks. Our culture is defined by ambitious, collaborative, and constantly evolving people who lead with trust, respect, and purpose.
Design, deploy, and optimize production-grade machine learning systems for the full ML lifecycle.
Build scalable MLOps platforms, CI/CD workflows, and model serving infrastructure.
Collaborate with engineering teams to improve platform scalability, security, and operational best practices.
They build scalable MLOps infrastructure for enterprise AI solutions. They foster a collaborative, remote-first culture focused on innovation and professional growth.
Architect scalable Python microservices and AI integrations on AWS.
Drive system health, code quality, and observability standards.
Mentor junior engineers and collaborate with US-based client teams.
CodeRoad provides end-to-end software development services, helping businesses scale with ideal infrastructure solutions. Our nearshore team culture empowers engineers to take ownership and grow continuously.
Design, deploy, and maintain scalable ML infrastructure for model training, batch processing, and real-time inference.
Build and manage cloud-based infrastructure with AWS and Snowflake using Infrastructure-as-Code practices.
Develop CI/CD pipelines, automation frameworks, and monitoring for ML systems to improve reliability and governance.
Jobgether is an AI-powered job matching platform that connects candidates with hiring companies. They operate with a team-oriented culture and offer remote work flexibility, focusing on efficient, unbiased recruitment.
Design, train, and ship ML systems for governance and security like anomaly detection and trust scoring.
Build data pipelines, model serving, evaluation frameworks, and feedback loops.
Set technical direction, own architecture, and help recruit and mentor as the team grows.
Docker provides developer tooling trusted by over 20 million monthly users and billions of container pulls. They are a globally distributed, remote-first team building tools for software delivery.
Design and deliver end-to-end data solutions for enterprise clients.
Build and maintain modern data lakes, warehouses, and analytical models.
Act as technical point of contact, mentor engineers, and drive quality.
The company is a consulting firm that delivers modern data platforms and analytics solutions for multinational clients. The culture emphasizes collaborative engineering, technical ownership, and work-life balance.