Machine Learning Engineer

Machine Learning Engineer

Requirements

• 2+ years of experience in software engineering, ML engineering, or data-focused development roles.
• Exposure to deploying machine learning models or supporting AI/ML workloads in production environments.
• Experience in client-facing roles or comfort working with external stakeholders.
• Proficient in Python, with experience building ML models or working with frameworks like PyTorch, TensorFlow, or similar.
• Familiarity with building or supporting production-grade ML systems, such as RAG pipelines or agent-based applications, is a plus.
• Solid understanding of core data science concepts, including statistical modeling, hypothesis testing, and data exploration techniques to inform model development and evaluation.
• Experience with cloud platforms (AWS, GCP, Azure), and a solid grasp of deployment workflows and infrastructure best practices.
• Familiarity with containerization tools (e.g., Docker, Kubernetes) is beneficial.
• Ability to write clean, modular code and contribute to automated tests (unit, integration, and end-to-end).
• Comfortable working with relational and/or NoSQL databases.
• Familiarity with MLOps concepts, including model tracking, monitoring, and versioning.
• Understanding of how DevOps principles apply to ML model development and deployment.
• Strong communication skills and a collaborative mindset, with the ability to engage effectively across internal teams and external client environments.

Responsibilities

• Contribute to the development of reliable, scalable backend systems that power machine learning workflows and data pipelines.
• Help manage and improve cloud infrastructure to support efficient model deployment and operational stability in real-world environments.
• Participate in identifying and addressing engineering challenges, including those surfaced through direct client feedback and usage.
• Work closely with ML engineers, data scientists, and external stakeholders to integrate machine learning capabilities into production systems.
• Assist in developing internal tools and infrastructure to streamline experimentation, training, and model serving across varied use cases.

Preferred Qualifications

• Familiarity with building or supporting production-grade ML systems, such as RAG pipelines or agent-based applications, is a plus.
• Familiarity with containerization tools (e.g., Docker, Kubernetes) is beneficial.