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. Youll 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.

Qualifications :

• 24 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; youre 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

Remote Work :

No

Employment Type :

Full-time