AI/ML Engineer | Talent Marketplace
Lago Open · verified Oct 22, 2026 At a glance
Mid-level Machine Learning Engineering role at Lago. Remote, Philippines.
Pay not stated Machine Learning Engineering salary
aimlroles summary, based on the employer's posting.
What you'll do
- Build and launch models for tasks such as classification, recommendations, language processing or computer vision
- Work with data engineers on pipelines that supply training and inference data
- Choose and tune model approaches, then test them for reliable performance on new data
- Put models into production, monitor results and address drift through retraining
- Connect AI features to applications and share experiment findings with technical and business audiences
What you bring
- Three to five years in machine learning engineering, data science or a related area
- You can build in Python with a core ML library such as scikit-learn, TensorFlow or PyTorch
- You understand core ML methods, model evaluation and feature engineering
- You have deployed ML models to production and use version control, testing and sound code practices
- You can work with data using tools such as Pandas, NumPy or SQL
Who this fits
This suits a mid-level engineer who can take machine learning work from data preparation through production monitoring and collaborate with data, product and engineering teams. The role is remote and open to professionals in the Philippines, Eastern Europe or Latin America.
From the employer
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AI/ML ENGINEER
Vertical: Tech
Location: Remote - Philippines, Eastern Europe, and Latin America
USD Salary: Negotiable based on experience
The AI/ML Engineer is responsible for designing, building, and deploying machine learning models and AI-powered systems that solve real business problems. This role spans the full ML lifecycle - from data preparation and model development to production deployment and monitoring - and requires a strong combination of software engineering discipline and data science expertise. The AI/ML Engineer collaborates closely with data engineers, product managers, and business stakeholders to deliver AI solutions that are accurate, reliable, and scalable.
KEY RESPONSIBILITIES
- Design, develop, and deploy machine learning models for classification, regression, NLP, computer vision, recommendation, or other applicable use cases.
- Work with data engineers to build and maintain data pipelines that feed ML model training and inference.
- Evaluate and select appropriate algorithms, frameworks, and architectures for each problem.
- Train, validate, and fine-tune models using best practices for avoiding overfitting and ensuring generalization.
- Deploy models to production environments and build robust inference pipelines.
- Monitor model performance post-deployment and implement strategies for model retraining and drift detection.
- Collaborate with product and engineering teams to integrate AI features into applications.
- Conduct experiments, document findings, and present insights to technical and non-technical stakeholders.
- Stay current with advances in AI/ML research and assess applicability to the business.
- Contribute to MLOps practices, tooling, and infrastructure.
Requirements
- 3–5 years of experience in machine learning engineering, data science, or a related field.
- Proficiency in Python and core ML libraries (scikit-learn, TensorFlow, PyTorch, or similar).
- Strong understanding of machine learning fundamentals (supervised/unsupervised learning, model evaluation, feature engineering).
- Experience deploying ML models to production environments (APIs, batch pipelines, or embedded systems).
- Familiarity with data manipulation and analysis (Pandas, NumPy, SQL).
- Solid software engineering practices - version control, testing, and code quality.
- Strong analytical and problem-solving skills.
NICE TO HAVE
- Experience with LLMs, prompt engineering, and generative AI applications (OpenAI, Anthropic, LangChain, or similar).
- Familiarity with MLOps platforms (MLflow, Weights & Biases, SageMaker, Vertex AI, or similar).
- Experience with cloud ML infrastructure (AWS, GCP, or Azure AI/ML services).
- Knowledge of data engineering tools and pipelines (Spark, Airflow, dbt, or similar).
- Experience with NLP techniques (transformers, embeddings, RAG pipelines).
- Advanced degree (M.S. or Ph.D.) in Computer Science, Statistics, or a related field.
Benefits
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