AI Engineer

About Vailent & Vinmar

Vinmar is a global leader in the marketing and distribution of polymers and chemicals, operating in over 100 countries with more than 45 years of success.

Vailent is a subsidiary of Vinmar, created to reimagine how polymers are bought and sold. We’re building a technology-enabled B2B marketplace that gives buyers and sellers a faster, more transparent, and more efficient way to do business. Our platform is designed to empower Vinmar’s global network while transforming an industry that has seen little digital innovation.

About the Role

At Vailent, you’ll join a small, agile, and fully remote team with the backing and stability of a global parent company. We combine the autonomy of a startup with the reach of a world leader. You’ll find the freedom to own your work, the support of approachable leadership, and the opportunity to solve complex challenges that directly impact how an entire industry operates.

As an AI Engineer, you’ll focus on building and deploying AI/ML solutions that power both customer-facing products and internal platforms. You’ll work across the stack—writing production-grade code, integrating models into applications, and ensuring solutions are reliable, secure, and performant. In this role, you’ll contribute to shaping how AI is applied within our products, while gaining experience in defining architecture and practices alongside senior and lead engineers. With access to state-of-the-art infrastructure and cutting-edge AI/ML tools, you’ll sharpen your technical skills, collaborate across teams, and grow toward leadership as you deepen your expertise in applying AI at scale.

Key Responsibilities

• Design, implement, and optimize AI/ML models for real-world applications.
• Integrate LLMs, generative AI, and ML models into production systems and user-facing applications.
• Collaborate with backend/frontend teams to deliver end-to-end AI-powered features.
• Own the AI/ML lifecycle: data collection, preprocessing, model training, evaluation, deployment, and monitoring.
• Build scalable cloud-based AI solutions using AWS SageMaker, Lambda, EC2, and other managed services.
• Develop CI/CD pipelines for AI workloads with GitHub Actions, Docker, and Kubernetes.
• Ensure AI systems follow best practices for security, performance, and responsible use (bias, fairness, explainability).
• Mentor engineers and share AI/ML knowledge across the team.

Why Join Us

• A culture that values technical excellence, ownership, and innovation.
• High impact, real ownership — Your work directly shapes the future of a global industry.
• Autonomy & trust — Freedom to own projects and make decisions.
• Team & culture — A collaborative environment where leadership is accessible, and people genuinely enjoy working together.

Hard Skills

• Software Development: Strong foundation in Python (AI/ML stack) plus production development experience in Java, Spring Boot, or similar.
• AI/ML Expertise: Practical experience with frameworks such as PyTorch, TensorFlow, scikit-learn, Hugging Face.
• AI APIs & Integrations: Hands-on experience with OpenAI, AWS AI, Vertex AI, or similar platforms.
• Cloud Infrastructure: Skilled in deploying and managing AI workloads on AWS (SageMaker, EC2, S3, RDS, Lambda).
• Data Engineering: Experience working with SQL/NoSQL databases, pipelines, and preprocessing for model training.
• MLOps & CI/CD: Proficiency with GitHub, GitHub Actions, Docker, Kubernetes; experience automating model training and deployment.

Soft Skills

• Strong collaboration across product, data, and engineering teams.
• Excellent communicator who can translate AI/ML concepts into actionable engineering tasks.
• Resourceful problem-solver with a “figure it out” mindset—able to work independently and own solutions.
• Demonstrates accountability, ownership, and a commitment to high-quality engineering.
• Adaptable and proactive learner, always staying ahead of emerging AI trends.
• Familiar with Agile/Kanban development practices.

Preferred

• Bachelor’s or Master’s in Computer Science, AI/ML, Data Science, or related field.
• Hands-on experience with LLMs, embeddings, RAG (retrieval-augmented generation), and vector databases (Pinecone, Weaviate, FAISS).
• Prior experience bringing AI systems into production at scale.
• Knowledge of responsible AI practices: fairness, bias mitigation, explainability.

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