ML Software engineer (Python)

We are looking for a motivated Machine Learning Engineer (Python) to join our fast-moving team. This is a hands-on role ideal for someone with a solid foundation in machine learning, strong Python skills, and a desire to grow in a dynamic and often ambiguous environment. You’ll work closely with product managers, data scientists, and engineers to rapidly prototype, test, and deploy ML-driven features and pipelines.

Responsibilities 

• Collaborate in an agile team to develop and deploy ML models and data pipelines.

• Translate loosely defined business or customer requirements into practical ML solutions.

• Be creative and proactive in shaping solutions that solve real-world problems.

• Build, test, and optimize scalable Python-based systems for training and inference.

• Work with structured and unstructured data (e.g., tabular, text, image).

• Participate in sprint planning, daily stand-ups, and reviews.

• Own and maintain parts of the codebase and contribute to improving engineering best practices.
  
  Collaboration 
  Work closely with product, engineering, and customer success teams to understand requirements and deploy ML models

• Communicate technical concepts and progress effectively to both technical and non-technical stakeholders.

• Collaborate on improving team processes, coding standards, and best practices.

• 2–4 years of experience in software engineering or ML engineering roles.

• Strong Python programming skills with experience in ML frameworks like scikit-learn, TensorFlow, or PyTorch.

• Proficiency with data handling libraries (e.g., pandas, NumPy) and tools like Jupyter.

• Ability to take customer or business requirements and creatively turn them into technical solutions.

• Exposure to common ML workflows: data cleaning, feature engineering, model evaluation.

• Comfortable working in a fast-paced, agile environment with changing priorities and some ambiguity.

• Good communication and collaboration skills; you’re proactive and not afraid to ask questions.

• Familiarity with version control (e.g., Git) and containerization (e.g., Docker).

• Experience with cloud platforms (AWS, GCP, or Azure) and basic understanding of MLOps practices.

• Exposure to CI/CD workflows and tools like MLflow or model serving frameworks.

• Ability to build lightweight APIs (e.g., FastAPI, Flask) to expose models as it enables seamless integration of machine learning solutions into production systems