Hire AI ML Developers
Access top AI ML Developers from LatAm with Lupa. Experts in model training, MLOps, and real-world AI applications onboarded remotely in just 21 days.
Hire Remote AI ML Developers
Martina, a skilled prompt engineer, excels in crafting precise, impactful solutions.
- Data Labeling
- NLP
- Python
- LLMs
- AI Ethics
Julio is an AI generalist applying smart systems to solve everyday challenges.
- Machine Learning
- AI Prototyping
- Data Pipelines
- Model Deployment
- Tech Integration
Milagros is an AI expert developing intelligent tools with ethical design principles.
- AI Ethics
- ML Workflow
- Data Annotation
- Collaborative Ideation
- Model Validation
Matías is a skilled prompt engineer, adept at crafting precise and impactful AI interactions.
- Python
- AI Ethics
- Data Labeling
- NLP
- LLMs
Yuliana is an AI expert designing intelligent systems that learn, adapt, and evolve.
- AI Development
- Model Optimization
- Ethical AI
- Technical Documentation
- Model Deployment
Natalia is an AI expert developing scalable, reliable, and human-friendly solutions.
- AI Strategy
- Machine Learning
- Neural Networks
- Product Development
- Data Science
Luis is an AI generalist creating functional systems with real-world applications.
- AI Strategy
- Machine Learning
- System Design
- Product Roadmapping
- Problem Solving
"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.
AI ML Developers Soft Skills
Analytical Thinking
Approach data problems with structured experimentation.
Curiosity
Stay informed on emerging ML methods and research.
Communication
Translate complex ML results into business impact.
Collaboration
Work effectively with data, product, and engineering teams.
Resilience
Iterate through failed experiments to find optimal models.
Time Management
Balance exploration with delivery timelines.
AI ML Developers Skills
Model Training & Evaluation
Train, tune, and assess models using structured datasets.
Supervised & Unsupervised Learning
Apply core ML methods across a range of data types.
Feature Engineering
Create and select meaningful features for model input.
Model Deployment
Deploy models into production using scalable tools and APIs.
Data Preprocessing
Clean and prepare raw data for ML pipeline readiness.
Performance Optimization
Improve speed, accuracy, and reliability of ML models.
How to Write an Effective Job Post to Hire AI ML Developers
Recommended Titles
- Machine Learning Engineer
- AI Software Developer
- ML Model Engineer
- AI Algorithm Developer
- ML Research Engineer
- Artificial Intelligence Engineer
Role Overview
- Tech Stack: Skilled in Python, TensorFlow, PyTorch, and Scikit-learn.
- Project Scope: Train, evaluate, and deploy ML models for classification and prediction use cases.
- Team Size: Join an applied ML team of 4–6 engineers and data scientists.
Role Requirements
- Years of Experience: At least 3 years in machine learning model development.
- Core Skills: Feature engineering, model tuning, and pipeline automation.
- Must-Have Technologies: TensorFlow, PyTorch, MLflow, Pandas, Docker.
Role Benefits
- Salary Range: $95,000 – $145,000 depending on depth and model experience.
- Remote Options: Flexible remote setup with async collaboration tools.
- Growth Opportunities: Involvement in real-world AI deployment and MLOps.
Do
- Include preferred ML libraries and model deployment tools
- Mention real-world ML project impact and use cases
- Highlight opportunities to work with large-scale datasets
- Emphasize team collaboration in AI model tuning
- Use targeted, data-centric language in job posts
Don't
- Don’t confuse AI research with practical ML implementation
- Avoid listing outdated libraries or irrelevant platforms
- Don’t exclude deployment or monitoring from scope
- Refrain from overemphasis on academic background
- Don’t use broad, non-technical phrasing
Top AI ML Developer Interview Questions
Key things to ask when hiring an AI ML Developer
What’s your process for selecting and tuning ML models?
Expect mention of cross-validation, hyperparameter tuning, and model selection criteria. Look for awareness of overfitting and interpretability.
Can you explain feature engineering in one of your recent projects?
Look for thoughtful use of domain knowledge, transformation techniques, and automated feature selection tools. Depth of reasoning is key.
Describe your experience with model deployment in production.
Candidates should mention APIs, Docker, CI/CD pipelines, and monitoring. Bonus if they’ve used MLOps tools like MLflow or SageMaker.
How do you handle imbalanced datasets?
Strong answers may include SMOTE, class weighting, resampling strategies, or evaluation with appropriate metrics (AUC, F1-score).
What metrics do you use to evaluate model performance?
Expect a tailored answer depending on the task (classification, regression). They should mention precision/recall, RMSE, ROC curves, etc.
How do you handle model underperformance after deployment?
Look for evaluation metrics, dataset drift analysis, and retraining procedures.
Describe a time when your model delivered unexpected results. What did you do?
Expect answers involving debugging data preprocessing, feature leakage, or labeling inconsistencies.
How do you troubleshoot training instability or loss divergence?
Look for learning rate tuning, architecture adjustments, or gradient clipping.
What’s your approach when data for a critical feature is missing or corrupted?
Expect imputation strategies, feature elimination, or data reconstruction using proxies.
How do you balance model complexity with interpretability?
Expect experience with interpretable ML models or tools like SHAP, LIME, and stakeholder-driven choices.
Tell me about a time a model you built failed in production.
Expect details on investigation, retraining, and communication with impacted teams.
How do you handle conflicting feedback from stakeholders on model outputs?
Expect prioritization strategies, data-backed communication, and iterative updates.
Describe a collaborative project where you had to integrate with engineering or product teams.
Look for examples of teamwork, shared timelines, and handoff practices.
How do you stay motivated when model training yields minimal improvement?
Expect signs of resilience, hypothesis reformulation, and long-term problem-solving mentality.
What’s an example of a difficult decision you made when building a pipeline?
Expect trade-offs involving scalability, latency, or interpretability, and rationale shared clearly.
- Weak grasp of model evaluation techniques
- Failure to validate data preprocessing pipelines
- Minimal exposure to production ML workflows
- Lack of reproducibility in experiments
- Dismissive of ethical or bias concerns
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 AI ML Developer Interview Questions
Key things to ask when hiring an AI ML Developer
What’s your process for selecting and tuning ML models?
Expect mention of cross-validation, hyperparameter tuning, and model selection criteria. Look for awareness of overfitting and interpretability.
Can you explain feature engineering in one of your recent projects?
Look for thoughtful use of domain knowledge, transformation techniques, and automated feature selection tools. Depth of reasoning is key.
Describe your experience with model deployment in production.
Candidates should mention APIs, Docker, CI/CD pipelines, and monitoring. Bonus if they’ve used MLOps tools like MLflow or SageMaker.
How do you handle imbalanced datasets?
Strong answers may include SMOTE, class weighting, resampling strategies, or evaluation with appropriate metrics (AUC, F1-score).
What metrics do you use to evaluate model performance?
Expect a tailored answer depending on the task (classification, regression). They should mention precision/recall, RMSE, ROC curves, etc.
How do you handle model underperformance after deployment?
Look for evaluation metrics, dataset drift analysis, and retraining procedures.
Describe a time when your model delivered unexpected results. What did you do?
Expect answers involving debugging data preprocessing, feature leakage, or labeling inconsistencies.
How do you troubleshoot training instability or loss divergence?
Look for learning rate tuning, architecture adjustments, or gradient clipping.
What’s your approach when data for a critical feature is missing or corrupted?
Expect imputation strategies, feature elimination, or data reconstruction using proxies.
How do you balance model complexity with interpretability?
Expect experience with interpretable ML models or tools like SHAP, LIME, and stakeholder-driven choices.
Tell me about a time a model you built failed in production.
Expect details on investigation, retraining, and communication with impacted teams.
How do you handle conflicting feedback from stakeholders on model outputs?
Expect prioritization strategies, data-backed communication, and iterative updates.
Describe a collaborative project where you had to integrate with engineering or product teams.
Look for examples of teamwork, shared timelines, and handoff practices.
How do you stay motivated when model training yields minimal improvement?
Expect signs of resilience, hypothesis reformulation, and long-term problem-solving mentality.
What’s an example of a difficult decision you made when building a pipeline?
Expect trade-offs involving scalability, latency, or interpretability, and rationale shared clearly.
- Weak grasp of model evaluation techniques
- Failure to validate data preprocessing pipelines
- Minimal exposure to production ML workflows
- Lack of reproducibility in experiments
- Dismissive of ethical or bias concerns
