Hire Machine Learning Engineers
Build fast with Machine Learning Engineers from LatAm. Proficient in model pipelines, feature engineering, and deployment with setup in 21 days.
Hire Remote Machine Learning Engineers
Jhonatan is an AI specialist building adaptive systems focused on performance.
- AI Model Tuning
- API Integration
- Data Engineering
- Solution Scaling
- Algorithm Development
Josefina is an AI specialist building intelligent systems with practical human benefits.
- AI Roadmapping
- Applied Machine Learning
- Data Strategy
- Feature Engineering
- Model Testing
Miguel is an AI specialist working on smart systems that improve user experiences.
- Machine Learning
- AI Strategy
- Product Roadmapping
- Data Modeling
- Problem Solving
Luis is an AI generalist creating functional systems with real-world applications.
- AI Strategy
- Machine Learning
- System Design
- Product Roadmapping
- Problem Solving
Jorge is an AI engineer designing automated systems with human-centered thinking.
- Automation Tools
- Machine Learning Engineering
- Predictive Analytics
- Tech Scalability
- Model Deployment
Julio is an AI generalist applying smart systems to solve everyday challenges.
- Machine Learning
- AI Prototyping
- Data Pipelines
- Model Deployment
- Tech Integration
Laura is a visionary AI researcher known for her innovative and insightful contributions.
- Computer Vision
- TensorFlow
- NLP
- Deep Learning
- Reinforcement Learning
"Over the course of 2024, we successfully hired 9 exceptional team members through Lupa, spanning mid-level to senior roles. The quality of talent has been outstanding, and we’ve been able to achieve payroll cost savings while bringing great professionals onto our team. We're very happy with the consultation and attention they've provided us."
“We needed to scale a new team quickly - with top talent. Lupa helped us build a great process, delivered great candidates quickly, and had impeccable service”
“With Lupa, we rebuilt our entire tech team in less than a month. We’re spending half as much on talent. Ten out of ten”
Lupa's Proven Process
Together, we'll create a precise hiring plan, defining your ideal candidate profile, team needs, compensation and cultural fit.
Our tech-enabled search scans thousands of candidates across LatAm, both active and passive. We leverage advanced tools and regional expertise to build a comprehensive talent pool.
We carefully assess 30+ candidates with proven track records. Our rigorous evaluation ensures each professional brings relevant experience from industry-leading companies, aligned to your needs.
Receive a curated selection of 3-4 top candidates with comprehensive profiles. Each includes proven background, key achievements, and expectations—enabling informed hiring decisions.
Reviews
"Over the course of 2024, we successfully hired 9 exceptional team members through Lupa, spanning mid-level to senior roles. The quality of talent has been outstanding, and we’ve been able to achieve payroll cost savings while bringing great professionals onto our team. We're very happy with the consultation and attention they've provided us."
“We needed to scale a new team quickly - with top talent. Lupa helped us build a great process, delivered great candidates quickly, and had impeccable service”
“With Lupa, we rebuilt our entire tech team in less than a month. We’re spending half as much on talent. Ten out of ten”
“We scaled our first tech team at record speed with Lupa. We couldn’t be happier with the service and the candidates we were sent.”
"Recruiting used to be a challenge, but Lupa transformed everything. Their professional, agile team delivers top-quality candidates, understands our needs, and provides exceptional personalized service. Highly recommended!"
“Lupa has become more than just a provider; it’s a true ally for Pirani in recruitment processes. The team is always available to support and deliver the best service. Additionally, I believe they offer highly competitive rates and service within the market.”
"Highly professional, patient with our changes, and always maintaining clear communication with candidates. We look forward to continuing to work with you on all our future roles."
“Lupa has been an exceptional partner this year, deeply committed to understanding our unique needs and staying flexible to support us. We're excited to continue our collaboration into 2025.”
"What I love about Lupa is their approach to sharing small, carefully selected batches of candidates. They focus on sending only the three most qualified individuals, which has already helped us successfully fill 7 roles.”
"We hired 2 of our key initial developers with Lupa. The consultation was very helpful, the candidates were great and the process has been super fluid. We're already planning to do our next batch of hiring with Lupa. 5 stars."
"Working with Lupa for LatAm hiring has been fantastic. They found us a highly skilled candidate at a better rate than our previous staffing company. The fit is perfect, and we’re excited to collaborate on more roles."
"We compared Lupa with another LatAm headhunter we found through Google, and Lupa delivered a far superior experience. Their consultative approach stood out, and the quality of their candidates was superior. I've hired through Lupa for both of my companies and look forward to building more of my LatAm team with their support."
“We’ve worked with Lupa on multiple roles, and they’ve delivered time and again. From sourcing an incredible Senior FullStack Developer to supporting our broader hiring needs, their team has been proactive, kind, and incredibly easy to work with. It really feels like we’ve gained a trusted partner in hiring.”
Working with Lupa was a great experience. We struggled to find software engineers with a specific skill set in the US, but Lupa helped us refine the role and articulate our needs. Their strategic approach made all the difference in finding the right person. Highly recommend!
Lupa goes beyond typical headhunters. They helped me craft the role, refine the interview process, and even navigate international payroll. I felt truly supported—and I’m thrilled with the person I hired. What stood out most was their responsiveness and the thoughtful, consultative approach they brought.
Machine Learning Engineers Soft Skills
Logical Thinking
Approach experiments with scientific structure.
Collaboration
Work with data, infra, and product teams on delivery.
Time Management
Balance research with production deliverables.
Communication
Convey trade-offs between speed, accuracy, and scope.
Accountability
Own model performance and ongoing improvements.
Adaptability
Shift approaches based on feedback or data shifts.
Machine Learning Engineers Skills
Model Development
Build supervised and unsupervised learning pipelines.
Feature Engineering
Create, clean, and select features for model input.
ML Ops Integration
Automate training, deployment, and monitoring steps.
Evaluation Metrics
Track model performance with precision and recall.
Data Pipeline Design
Develop scalable data flows for ML ingestion.
Cloud ML Deployment
Use AWS, GCP, or Azure for scalable model serving.
How to Write an Effective Job Post to Hire Machine Learning Engineers
Recommended Titles
- ML Engineer
- AI Model Developer
- Data Science Engineer
- ML Ops Engineer
- AI Systems Developer
- Predictive Modeling Engineer
Role Overview
- Tech Stack: Skilled in Scikit-learn, TensorFlow, Pandas, SQL, and Python.
- Project Scope: Build pipelines for data preprocessing, training, evaluation, and model deployment.
- Team Size: Work with data scientists and back-end engineers in teams of 5–8.
Role Requirements
- Years of Experience: 3+ years of ML model engineering and deployment.
- Core Skills: Model tuning, feature engineering, pipeline automation, and evaluation metrics.
- Must-Have Technologies: Scikit-learn, MLflow, TensorFlow, Airflow, Docker.
Role Benefits
- Salary Range: $95,000 – $150,000 depending on tooling and vertical experience.
- Remote Options: Fully remote with team alignment tools and async ops.
- Growth Opportunities: Be part of scalable machine learning systems from concept to production.
Do
- List core skills: model building, deployment, and monitoring
- Mention experience with end-to-end ML pipelines
- Include preferred tools like Scikit-learn, TensorFlow, MLflow
- Highlight performance tuning and model optimization
- Use practical, scalable ML terminology
Don't
- Don’t generalize ML without specifying production context
- Avoid skipping model evaluation and data pipeline tasks
- Don’t list tools without linking them to deliverables
- Refrain from using catch-all roles like “Data Scientist”
- Don’t omit collaboration with data and dev teams
Top Machine Learning Engineer Interview Questions
Smart questions to assess Machine Learning Engineer skills
How do you design a scalable ML pipeline?
Expect modular design, automation (CI/CD), and tools like MLflow, Airflow, or Kubeflow. Look for deployment considerations.
What’s your experience with feature stores?
Look for understanding of consistency, offline/online sync, and tools like Feast or custom solutions.
How do you ensure data quality in ML workflows?
Expect discussion on data validation, monitoring, drift detection, and logging systems.
What tools do you use for model versioning and tracking?
Strong answers include MLflow, DVC, Weights & Biases, and explain how tracking fits into reproducibility goals.
How do you work with cross-functional teams in ML projects?
They should demonstrate clear communication with data teams, product, and DevOps to align models with production use cases.
How do you debug data pipeline issues affecting model accuracy?
Look for validation checks, schema mismatches, feature drift tracking, and logging at preprocessing steps.
Describe a time you deployed a model that failed in production. What did you do?
Expect monitoring, rollback strategies, and incident postmortem with retraining cycles.
What’s your approach when feature importance doesn’t match expectations?
Expect feature engineering review, domain expert input, or explainability tooling like SHAP or LIME.
How do you ensure robustness when retraining models periodically?
Expect versioning, retraining policies, model validation suites, and regression checks.
How do you handle inconsistent performance across customer segments?
Expect segment-specific evaluation, fairness audits, and model adaptations per cohort.
Describe a time you collaborated across teams to ship a model to production.
Expect coordination with MLOps, product managers, and devs—plus ownership of handoff success.
Tell me about a time your deployment failed—what did you learn?
Look for ownership, alerting improvements, and resilience through iteration.
How do you handle tension between research and engineering constraints?
Expect pragmatism, backlog balancing, and milestone planning.
Describe how you respond when metrics stop improving.
Expect root cause analysis, feature engineering pivots, or label strategy reviews.
How do you manage technical debt in ML pipelines?
Look for scheduled cleanup, modular design, and team-wide knowledge sharing.
- Weak integration between model and infrastructure
- Ignores version control for datasets and models
- Lack of CI/CD understanding for ML pipelines
- Fails to monitor model drift or accuracy decay
- Resistant to using standardized tools or frameworks
Build elite teams in record time, full setup in 21 days or less.
Book a Free ConsultationWhy We Stand Out From Other Recruiting Firms
From search to hire, our process is designed to secure the perfect talent for your team
Local Expertise
Tap into our knowledge of the LatAm market to secure the best talent at competitive, local rates. We know where to look, who to hire, and how to meet your needs precisely.
Direct Control
Retain complete control over your hiring process. With our strategic insights, you’ll know exactly where to find top talent, who to hire, and what to offer for a perfect match.
Seamless Compliance
We manage contracts, tax laws, and labor regulations, offering a worry-free recruitment experience tailored to your business needs, free of hidden costs and surprises.
Lupa will help you hire top talent in Latin America.
Book a Free Consultation
Top Machine Learning Engineer Interview Questions
Smart questions to assess Machine Learning Engineer skills
How do you design a scalable ML pipeline?
Expect modular design, automation (CI/CD), and tools like MLflow, Airflow, or Kubeflow. Look for deployment considerations.
What’s your experience with feature stores?
Look for understanding of consistency, offline/online sync, and tools like Feast or custom solutions.
How do you ensure data quality in ML workflows?
Expect discussion on data validation, monitoring, drift detection, and logging systems.
What tools do you use for model versioning and tracking?
Strong answers include MLflow, DVC, Weights & Biases, and explain how tracking fits into reproducibility goals.
How do you work with cross-functional teams in ML projects?
They should demonstrate clear communication with data teams, product, and DevOps to align models with production use cases.
How do you debug data pipeline issues affecting model accuracy?
Look for validation checks, schema mismatches, feature drift tracking, and logging at preprocessing steps.
Describe a time you deployed a model that failed in production. What did you do?
Expect monitoring, rollback strategies, and incident postmortem with retraining cycles.
What’s your approach when feature importance doesn’t match expectations?
Expect feature engineering review, domain expert input, or explainability tooling like SHAP or LIME.
How do you ensure robustness when retraining models periodically?
Expect versioning, retraining policies, model validation suites, and regression checks.
How do you handle inconsistent performance across customer segments?
Expect segment-specific evaluation, fairness audits, and model adaptations per cohort.
Describe a time you collaborated across teams to ship a model to production.
Expect coordination with MLOps, product managers, and devs—plus ownership of handoff success.
Tell me about a time your deployment failed—what did you learn?
Look for ownership, alerting improvements, and resilience through iteration.
How do you handle tension between research and engineering constraints?
Expect pragmatism, backlog balancing, and milestone planning.
Describe how you respond when metrics stop improving.
Expect root cause analysis, feature engineering pivots, or label strategy reviews.
How do you manage technical debt in ML pipelines?
Look for scheduled cleanup, modular design, and team-wide knowledge sharing.
- Weak integration between model and infrastructure
- Ignores version control for datasets and models
- Lack of CI/CD understanding for ML pipelines
- Fails to monitor model drift or accuracy decay
- Resistant to using standardized tools or frameworks
