AI/ML Engineer

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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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