Hire ML Engineers

Hire ML Engineers

Hire senior ML engineers from Latin America in 8-12 days. Build custom machine learning models, MLOps pipelines, and production ML systems that drive business outcomes.

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TL;DR

Hire ML engineers in 8-12 days: Get vetted, senior-level ML engineers from Latin America who train custom models, build MLOps pipelines, and deploy production machine learning systems. Work in US time zones, cost 40-60% less than domestic hires. Meet qualified candidates in 48 hours.

Key Benefits:

  • Fast Placement: Meet candidates in 48 hours, fully onboarded in 8-12 days
  • Senior Expertise: 5+ years experience, Python, TensorFlow/PyTorch, MLOps, production ML
  • Model Development: Custom models, feature engineering, hyperparameter tuning, deployment
  • Cost Effective: $6,000-9,000/month vs $15,000-18,000+ for US ML engineers
  • Time Zone Aligned: 0-3 hour difference for real-time collaboration
  • Retention Guarantee: Free replacement within 90 days
  • Full Support: Payroll, benefits, HR, equipment, team retention

Why Hire ML Engineers Through Ideaware?

Finding ML engineers who can train production-grade models—not just run notebooks on Kaggle datasets—is challenging. You need engineers who understand the full ML lifecycle: data preparation, feature engineering, model selection, training, evaluation, deployment, monitoring, and continuous improvement.

At Ideaware, we’ve vetted thousands of developers across Latin America. We maintain a pipeline of 250+ ML engineers ready to interview this week.

We Actually Vet for ML Engineering Expertise: Not just scikit-learn tutorials. We test for model architecture design, feature engineering, training pipeline development, hyperparameter optimization, MLOps practices, and production deployment with monitoring.

We Move Fast: 48 hours to candidates. 8-12 days to onboarded engineers.

We Handle Everything: Payroll, benefits, equipment, HR, retention, career development.

We Guarantee Results: Free replacement if it doesn’t work out. 2x industry average retention rate.

What Our ML Engineers Do

  • Design and train custom ML models using TensorFlow, PyTorch, scikit-learn for classification, regression, clustering, and time-series forecasting
  • Build feature engineering pipelines to transform raw data into model-ready features with proper validation and reproducibility
  • Implement MLOps workflows for model versioning, experiment tracking, automated training, and deployment pipelines
  • Deploy production ML systems with model serving infrastructure, A/B testing, shadow deployments, and rollback strategies
  • Optimize model performance through hyperparameter tuning, architecture search, ensemble methods, and training efficiency
  • Develop data pipelines for ML model training, feature stores, and data quality monitoring
  • Create evaluation frameworks with proper metrics, validation strategies, and bias detection
  • Monitor production models for data drift, model degradation, performance issues, and retraining triggers
  • Build AutoML systems for automated model selection, feature engineering, and hyperparameter optimization
  • Implement deep learning models including CNNs, RNNs, Transformers for computer vision, NLP, and sequence modeling
  • Collaborate with data scientists to productionize research models and scale them to production

When to Hire ML Engineers

Custom Model Development: When off-the-shelf models don’t meet your needs—domain-specific predictions, proprietary algorithms

Recommendation Systems: Personalized recommendations for e-commerce, content platforms, streaming services

Predictive Analytics: Forecasting demand, churn prediction, fraud detection, risk scoring

Computer Vision: Image classification, object detection, OCR, visual search, quality control

NLP Applications: Text classification, sentiment analysis, entity extraction, document understanding (when LLM APIs aren’t sufficient)

Time Series: Demand forecasting, anomaly detection, predictive maintenance, financial modeling

MLOps Infrastructure: Building scalable ML platforms for data science teams

Common Tech Stack & Skills

ML Frameworks:

  • TensorFlow / Keras
  • PyTorch / Lightning
  • scikit-learn
  • XGBoost, LightGBM, CatBoost
  • Hugging Face Transformers

MLOps & Deployment:

  • MLflow
  • Kubeflow
  • AWS SageMaker
  • Azure ML
  • GCP Vertex AI
  • Docker, Kubernetes

Data Processing:

  • Pandas, NumPy
  • Spark, Dask
  • Feature stores (Feast, Tecton)
  • Data validation (Great Expectations)

Model Serving:

  • TensorFlow Serving
  • TorchServe
  • FastAPI, Flask
  • ONNX Runtime
  • Triton Inference Server

Experiment Tracking:

  • Weights & Biases
  • MLflow
  • Neptune.ai
  • TensorBoard

Cloud Platforms:

  • AWS (SageMaker, EC2, S3, Lambda)
  • GCP (Vertex AI, BigQuery ML)
  • Azure (Azure ML, Databricks)

Programming:

  • Python (primary)
  • SQL
  • Bash scripting
  • Git version control

Math & Statistics:

  • Linear algebra
  • Probability & statistics
  • Optimization
  • Model evaluation metrics

Pricing & Engagement

Senior ML Engineers: $6,000-9,000/month

  • Full-time (40 hrs/week)
  • Benefits & payroll
  • HR support
  • Equipment
  • Retention programs

Compare to US ML engineers: $160,000-220,000/year = $13,333-18,333/month

Your savings: 40-55% without sacrificing quality.

Frequently Asked Questions

How much does it cost to hire ML engineers?

Senior ML engineers cost $6,000-9,000/month including full-time employment, benefits, HR support, and retention programs. This is 40-55% less than US-based ML engineers with no recruiting fees. ML engineers command premium rates due to specialized mathematical and engineering expertise.

How quickly can I hire ML engineers?

48 hours to vetted profiles. 8-12 days to fully onboarded. 10-15 weeks faster than traditional hiring.

What skills should ML engineers have?

Essential: Python, TensorFlow/PyTorch, scikit-learn, feature engineering, model evaluation, MLOps (MLflow/Kubeflow), cloud platforms (SageMaker/Vertex AI), SQL, statistics, production deployment, monitoring. Bonus: deep learning, NLP, computer vision, distributed training.

Are nearshore ML engineers as good as US developers?

Yes. Many hold advanced degrees (MS/PhD), 70%+ have US company experience, work in US time zones with strong English. The ML field is inherently global—engineers learn from the same papers, frameworks, and best practices. 80% hire rate, 2x retention rate.

What is your vetting process for ML engineers?

Only 3% pass: (1) ML technical assessment (model design, feature engineering, evaluation), (2) 90-min live coding building an ML pipeline, (3) architecture review of production ML system, (4) portfolio review of deployed models, (5) cultural fit evaluation, (6) reference checks. We test practical ML engineering, not just theoretical knowledge.

What time zones do ML engineers work in?

0-3 hour difference from US time zones. Real-time collaboration during business hours for experiment reviews, model debugging, and sprint planning.

Can I hire a full ML team?

Yes. Solo ML engineers, pairs, or complete teams (2-3 ML engineers + data engineers + data scientists + MLOps specialists). Scale as needed.

What if it doesn't work out?

Free replacement within 90 days. No fees or penalties.

ML Engineers vs AI Engineers vs Data Scientists: What's the difference?

ML Engineers focus on building and deploying custom ML models—training pipelines, feature engineering, model optimization, MLOps infrastructure, production deployment. Software engineers who specialize in machine learning systems.

AI Engineers focus on integrating pre-trained AI (especially LLMs)—RAG systems, prompt engineering, LLM APIs, vector databases, AI-native applications. Software engineers who build with existing AI models.

Data Scientists focus on research and analysis—exploratory data analysis, statistical modeling, business insights, experiment design, model prototyping. Often hand off to ML Engineers for productionization.

When to hire ML Engineers:

  • Custom model development for your data
  • MLOps infrastructure and pipelines
  • Traditional ML (not just LLMs): recommendations, forecasting, classification
  • When you need models trained on proprietary data
  • Scaling and optimizing existing models

When to hire AI Engineers:

  • Building with OpenAI, Claude, other LLM APIs
  • RAG systems and chatbots
  • Faster time-to-market with pre-trained models
  • AI features, not custom ML research

When to hire Data Scientists:

  • Exploratory analysis and insights
  • Experiment design and A/B testing
  • Statistical modeling and business intelligence
  • Early-stage model prototyping

Our take: Many companies need both. Data Scientists explore and prototype, ML Engineers productionize and scale. For 2025 AI products, AI Engineers provide fastest time-to-market; ML Engineers become essential when custom models outperform generic LLMs.

What ongoing support do you provide?

Full HR support (payroll, benefits), career development (ML conferences, training), retention programs, operational support, strategic partnership (quarterly reviews, pipeline).

What are the contract terms?

Month-to-month with 30-day notice. No long-term commitments, no placement fees. Free 90-day replacement guarantee.

Who owns the ML models and code?

You own 100%. All IP, code, trained models, and training data assigned to your company with NDAs and secure protocols.

How good is the English communication?

Business-level required. Clients rate 4.7/5. Full participation in technical discussions without friction.

Do you provide junior or only senior ML engineers?

Primarily senior (5+ years): $6,000-9,000/month. Also: mid-level (3-5 years): $5,000-7,000/month. Junior ML engineers rarely recommended given complexity of production ML systems.

Do ML engineers handle MLOps or just model development?

Senior ML engineers handle both. They design models AND build the infrastructure to deploy, monitor, and maintain them. This includes: experiment tracking (MLflow, Weights & Biases), model versioning, CI/CD for ML, automated retraining pipelines, feature stores, model serving infrastructure, monitoring dashboards, and A/B testing frameworks. Modern ML engineering is end-to-end ownership, not just notebook development.

Next Steps

  1. Book Discovery Call - 30 minutes to discuss your ML needs
  2. Meet Candidates - 48 hours to receive vetted ML engineer profiles
  3. Interview & Select - Week 1, you control the process
  4. Onboard & Ship - Week 2, engineer integrated into your team
  5. Scale as Needed - Add more ML engineers or build complete ML teams

Schedule your discovery call and meet qualified ML engineers in 48 hours.