Machine Learning Engineer

Duties

• Apply established machine learning and AI techniques to new problems and datasets.
• Build, optimize, and maintain machine learning and AI models and supporting pipelines.
• Evaluate and monitor ML/AI system outcomes, model performance, and data quality; define appropriate metrics and acceptance criteria.
• Identify issues in models, pipelines, and datasets; recommend and implement improvements.
• Design, develop, test, document, refactor, and maintain moderately complex programs/scripts to support ML development and deployment.
• Follow agreed engineering standards, tools, and best practices to deliver secure, reliable, and maintainable solutions.
• Monitor progress, report status, and communicate risks, blockers, and dependencies in a timely manner.
• Collaborate with teammates through code reviews, design reviews, and shared ownership of deliverables.
• Elicit requirements for ML/AI lifecycle practices, working methods, and automation (e.g., CI/CD, testing, deployment, monitoring).
• Select and implement appropriate lifecycle practices for components and microservices within the ML/AI solution.
• Deploy automation to support well-engineered, repeatable, and secure build/release processes.
• Define ML/AI modules needed for integration builds and produce buIld definitions for each release/generation of the solution.
• Validate and accept completed ML/AI modules against agreed functional, quality, and performance criteria.
• Apply data science techniques to new problems and datasets, using specialized programming approaches where needed.
• Identify and implement opportunities to improve training data, features, and model performance.
• Build and maintain data pipelines using data engineering standards and tools (ETL/ELT).
• Support monitoring of emerging technologies and contribute to internal reports, technology roadmaps, and knowledge sharing.

Requirement

• 5 years of hands-on experience building ML/AI solutions in Python, with strong foundations in machine learning concepts, software engineering, and production-grade development practices.
• Proven experience designing, developing, optimizing, and maintaining end-to-end AI/ML pipelines (data processing, training, evaluation, deployment, and monitoring).
• Strong track record in model evaluation and performance measurement, including defining metrics, running assessments, and monitoring model quality over time.
• Experience applying and adapting pre-trained models (including Generative AI/LLMs) to solve specific business use cases.
• Solid experience with MLOps practices: version control, experiment tracking, model packaging, deployment, monitoring, and automation.
• Proficiency with CI/CD pipelines and DevOps best practices (e.g., Git-based workflows, build/release automation).
• Practical experience with containerization (Docker, Podman) and orchestration using Kubernetes, including infrastructure provisioning and operationalization in cloud environments.
• Experience with workflow orchestration tools such as Apache Airflow and/or Argo Workflows.
• Strong experience building and maintaining REST APIs, ideally for serving ML models and AI services.
• Experience working with SQL and NoSQL databases.

Preferences

• Experience building production-grade AI agent backends, e.g., using LangChain or pydantic-ai, wrapped in FastAPI services.
• Full-stack experience with TypeScript frameworks such as Next.js.
• Experience working in air-gapped / restricted-network environments.