Why choose Ideaware for MLOps talent
For over 12 years, we have helped American companies scale their software teams and grow their businesses. Our focus is on seamlessly connecting you with the ideal MLOps talent who not only possesses the right skills but also aligns with your project’s personality, culture, and expectations.
From day one, you and your team work with our expert team of recruiters and HR to meet your needs to the tee. There are no up-front fees to get started. Our commitment is demonstrated by the fact that you pay only after the first month your candidate is onboard.
15+
years in business
4y+
client engagement length
4.6 y
average retention time
1,250
filled roles
Struggling to find top talent on your own? Skip the recruitment hassle
Access top-tier, pre-screened professionals within 48 hours of sharing your job details. All candidates are sourced from our 12-year-strong database and network in Colombia.
Hire talent
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Luis P.
Frontend / Mobile Developer
- ReactJS
- React Native
Luis is a Mechanical Engineer with over 4 years of experience working with multidisciplinary and multicultural teams. He has knowledge of SCRUM for project planning and execution.
Chile
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Daniel C.
Backend Developer
- NodeJS
- Typescript
Daniel has over 6 years of experience working as a backend developer, mainly using NodeJS. He's adept at tackling diverse IT challenges and is oriented to fulfilling project objectives.
Colombia
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Laura V.
Product Designer
- Figma
Laura has over 7 years of experience as a Product Designer. She is skilled at identifying user needs, researching, and creating wireframes and prototypes to optimize user interactions.
Argentina
The ultimate guide for hiring MLOps engineers
Are you seeking to accelerate the growth and scale your development team? Recognizing the pivotal role talent plays in project success, we've crafted a guide to enhance your understanding of the hiring process. Explore insights on what to anticipate from experts in technical and soft skills and responsibilities, along with FAQs. We will give you a whole different perspective!
Before you start hiring
Access top-tier, pre-screened professionals within 48 hours of sharing your job details. All candidates are sourced from our 15-year-strong database and network in Latin America.
Define your project requirements
Clearly define your project goals, scope, and technical requirements. The difficulty level and the type of task you're dealing with will determine the skills and expertise needed.
Culture fit
Your hires must align with your company goals, values, and team culture. Someone who can integrate seamlessly into your team will adapt faster to your workflow and be more productive.
Budget planning
Establish a budget for your experts. Consider factors like labor costs, project timeline, infrastructure, and potential travel expenses.
Team proximity
Decide between outsourcing IT talent or in-house hiring. A hybrid approach, which combines these two, is also a viable option in some cases, providing a balance between control and flexibility.
Technical skills every MLOps engineer should have
Your MLOps engineer needs a range of skills to manage day-to-day tasks and protect your software’s future. When they have the right technical skills, your projects will flow effortlessly, with top-quality code and minimal supervision.
- Experience with ML model deployment and serving
- Proficiency in Docker, Kubernetes, and cloud platforms
- Knowledge of CI/CD pipelines for ML systems
- Understanding of model monitoring and observability
- Familiarity with ML frameworks (TensorFlow, PyTorch)
- Expertise in infrastructure as code and automation
Skills that go beyond code
Communication skills
MLOps engineers who can bridge the gap between data science and operations teams, document complex ML pipelines, and communicate technical requirements clearly.
Problem-solving and critical thinking
Engineers who can troubleshoot complex ML deployment issues, optimize pipeline performance, and design scalable ML infrastructure solutions.
Teamwork and collaboration
MLOps engineers who work effectively with data scientists, DevOps teams, and business stakeholders to ensure smooth ML model deployment and operations.
Time management and organization
Engineers who can manage multiple ML deployment projects, prioritize critical issues, and maintain reliable ML systems under pressure.
Attention to detail
Engineers who meticulously monitor ML system performance, ensure data quality in production, and maintain high standards in deployment processes.
Communication skills
MLOps engineers who can bridge the gap between data science and operations teams, document complex ML pipelines, and communicate technical requirements clearly.
Problem-solving and critical thinking
Engineers who can troubleshoot complex ML deployment issues, optimize pipeline performance, and design scalable ML infrastructure solutions.
Teamwork and collaboration
MLOps engineers who work effectively with data scientists, DevOps teams, and business stakeholders to ensure smooth ML model deployment and operations.
Time management and organization
Engineers who can manage multiple ML deployment projects, prioritize critical issues, and maintain reliable ML systems under pressure.
Attention to detail
Engineers who meticulously monitor ML system performance, ensure data quality in production, and maintain high standards in deployment processes.
Responsibilities of MLOps engineers
Our MLOps engineers focus on building robust, scalable infrastructure for machine learning systems that enable reliable model deployment and monitoring.
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ML pipeline development and automation
Design and implement automated ML pipelines for model training, testing, and deployment using CI/CD best practices.
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Model deployment and serving
Deploy ML models to production environments, implement model serving infrastructure, and ensure high availability and performance.
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Monitoring and observability
Implement comprehensive monitoring systems for ML models, track performance metrics, and set up alerting for model drift and issues.
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Infrastructure management
Manage cloud infrastructure for ML workloads, optimize resource utilization, and ensure cost-effective scaling of ML systems.
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Security and compliance
Implement security best practices for ML systems, ensure data privacy compliance, and maintain audit trails for model deployments.
How it works
Getting started with us is easy and there are no strings attached or up-front costs. We take care of sourcing, screening and legal/tax compliance while you focus on your business.
01
Job descriptions
Let us know what type of talent you need. We will build the perfect job profile for the role.
02
Screening
We take a multi-channel approach to sourcing, screening and finding only those candidates who are a perfect match.
03
Interviews
We set up as many interviews as you decide to have with potential candidates. Your process, your decision.
04
Onboarding
Once you give us the green light, we onboard your new members. We take care of all legal and tax compliance.
Hire vetted MLOps engineers for your machine learning operations with Ideaware. Discover the advantages of outsourcing with us.
Get in touch
Let's build your dream team today
Hire expert developers in your tech stack, aligned with your time zone. We handle payroll, benefits, and compliance.