Design, build, and automate enterprise-grade AWS SageMaker environments to support scalable machine learning initiatives.
Develop and implement DevOps automation for SageMaker Unified Studio and related cloud infrastructure.
Build and optimize CI/CD pipelines for deploying custom Docker images, kernels, and machine learning workloads.
This role is listed on behalf of a partner company that builds and optimizes enterprise-scale machine learning infrastructure. They operate with a collaborative international team and modern engineering practices.
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.
Design and deliver production-grade AI/ML and GenAI solutions on cloud platforms like AWS, ensuring scalability and business value.
Act as a senior technical advisor for enterprise customers, translating challenges into secure, cost-efficient cloud architectures.
Develop reusable frameworks and best practices from delivery work to scale successful solutions across customers.
Jobgether is an AI-powered job matching platform that connects candidates with hiring companies. The company operates with a global, remote-first team focused on efficient recruitment.
Design, build, and deploy production ML and LLM-based systems (RAG, agentic workflows, fine-tuning, embeddings) for enterprise clients.
Own technical delivery end-to-end: from architecture and prototyping to deployment, monitoring, and iteration.
Mentor and support other ML engineers on the team with code reviews, technical guidance, and knowledge sharing.
TensorOps is a boutique AI consultancy that bridges strategy and execution, designing and shipping production-grade AI systems for enterprise clients. We are a 100% remote team of 11+ people, partnering with unicorns and NASDAQ-listed companies, and have a culture of autonomy, open communication, and continuous learning.
You will experiment with emerging technologies and contribute to building new models and systems.
You will implement prototypes in Python and focus on delivering solutions to production.
You will partner with the platform engineering team to streamline MLOps workflows and maintain high code quality.
Verve creates a more efficient and privacy-focused way to buy and monetize advertising by fusing data, media, and technology. With 30 offices globally, they serve top advertisers and publishers and foster a collaborative, fun culture.
Lead data initiatives using machine learning and AI to optimize ad delivery and platform performance.
Design, build, and maintain scalable AWS data pipelines for ML tooling and high-velocity ad systems.
Productionize forecasting and optimization models with robust backtesting, monitoring, and guardrail systems.
AdsByNimbus is a leader in Publisher-first mobile AdTech. The team is lean, highly capable, and deeply collaborative, offering high levels of project ownership and autonomy.
Design and maintain scalable ML infrastructure including data pipelines, training workflows, and model deployment systems.
Own end-to-end ML lifecycle operations, ensuring reliable delivery of models into production at scale.
Implement monitoring, telemetry, and feedback loops for ML models running across large-scale device fleets.
Our partner company develops ML systems for connected hardware products used by customers worldwide. They operate in a fast-paced, product-driven environment with a collaborative and technically ambitious culture focused on real-world ML impact.
Design, build, and maintain scalable data and ML pipelines for analytics and AI systems.
Build and optimize workflows for structured and unstructured data, enabling semantic search and RAG use cases.
Manage and optimize vector databases and indexing strategies for efficient retrieval and AI-powered search.
This is a partner company seeking a Data & Machine Learning Engineer based in Brazil. They operate in a highly technical and global environment with strong emphasis on scalability, performance, and innovation.
Design, build, and maintain scalable machine learning infrastructure on AWS, including training and deployment pipelines.
Develop and deploy ML models for recommendation systems, fraud detection, credit risk, and personalization use cases.
Implement monitoring, logging, and alerting systems to ensure model performance, stability, and reliability in production.
Our partner is a fast-growing, innovation-driven company where machine learning and AI systems directly power large-scale fintech and commerce experiences. They foster a highly dynamic environment with strong emphasis on experimentation, rapid iteration, and measurable business impact.
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.
Lead the design and development of scalable AI solutions, from experimentation to production deployment.
Define AI engineering standards and best practices, influencing architecture decisions across teams.
Collaborate with cross-functional stakeholders to integrate generative AI and LLM capabilities into products.
The company is at the forefront of AI-driven product development, focusing on building scalable and intelligent systems. It fosters a culture of innovation and technical excellence, with a remote team and a commitment to engineering leadership.
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.
Design and develop machine learning models for localization workflows, including machine translation and LLM finetuning.
Implement and optimize models using Python, TensorFlow, and deploy via Docker and AWS services.
Evaluate and select ML techniques, perform statistical analysis, and maintain clear documentation.
Welo Global is a leader in multilingual AI, technology, and content solutions serving over 2,000 clients in 300 languages. The company combines globally scaled multilingual infrastructure with a network of over 500,000 linguists and domain experts, backed by seven ISO certifications.
Lead the design and implementation of scalable AI-powered solutions across the product ecosystem.
Automate manual workflows using AI-driven approaches, ensuring measurable efficiency gains.
Architect and build semantic data models and agent-based architectures for intelligent task automation.
Sigma Software is a technology company specializing in AI-powered product development. They foster a culture of innovation, ownership, and engineering excellence with a highly skilled international team.
Lead the design and implementation of production-grade cloud and AI/ML solutions on AWS, ensuring scalability, security, and cost efficiency.
Act as a trusted technical advisor for customers, optimizing reliability, performance, security, and FinOps outcomes.
Deliver structured engagements such as architecture reviews, cloud optimization assessments, and GenAI workshops.
Jobgether is an AI-powered job matching platform that connects candidates with hiring companies. It uses technology to review applications and share shortlists with employers, operating with a global, distributed team.
Design and maintain scalable data pipelines for ingestion, transformation, and delivery into data warehouses, feature stores, and ML/AI systems.
Build workflows for processing unstructured data and develop semantic representations to enable advanced search, retrieval, and LLM-powered applications.
Collaborate with stakeholders to translate business requirements into scalable data and ML solutions.
Jobgether is an AI-powered job matching platform that connects candidates with hiring companies. They use technology to review applications and share top-fitting candidates directly with employers, ensuring a fair and efficient hiring process.
Build AI platform and production systems supporting computer vision, perception, simulation, and mapping products.
Own critical parts of the AI platform, including orchestration, APIs, and deployment patterns.
Partner with data scientists and simulation engineers to reduce iteration friction and design production architecture.
HERE Technologies is a location data and technology platform company that empowers customers to achieve better outcomes. The company is an equal opportunity employer with a focus on innovation and inclusion.
Build and maintain end-to-end deployment pipelines for AI-powered applications, including artifact builds, environment promotion, rollback, and observability hooks.
Stand up and operate the runtime and lifecycle infrastructure for production agents, including deployment, versioning, monitoring, rate-limiting, and retirement.
Design and build the shared developer harness that every AI-powered service uses: prompt management, model routing, retries, tracing, eval hooks, and policy enforcement.
RxSense is a healthcare technology company that provides platforms and solutions to improve the management and access of cost-effective pharmacy benefits. As a leader in SaaS technology for healthcare, the company offers innovative solutions with integrated intelligence on a single enterprise platform, connecting the pharmacy ecosystem.
Develop and operate production-ready AI and machine learning systems for enterprise-scale products.
Build and optimize LLM-powered applications, RAG pipelines, and intelligent agents.
Implement software engineering best practices for AI development including CI/CD and testing.
Our partner is building enterprise-grade AI solutions that deliver measurable business impact. They offer a remote-friendly work environment with a collaborative engineering culture focused on innovation, quality, and continuous learning.
Lead the design and operation of production machine learning systems for batch and online use cases with a focus on reliability and scalability.
Build and improve ML lifecycle infrastructure including training pipelines, inference workflows, monitoring, and automation.
Partner with cross-functional teams to translate business problems into ML solutions and guide prototypes to robust production systems.
Included Health is a healthcare company delivering integrated virtual care and navigation, aiming to raise the standard of healthcare for everyone. They are a remote-first organization offering comprehensive benefits and fostering a culture of inclusion.