Sr. Machine Learning Engineer

Sr. Machine Learning Engineer (Full Stack Developer) (5)

We are seeking a Machine Learning Engineer with strong Python expertise and full-stack development 

capabilities to join our team. In this role, you will design, develop, and deploy scalable machine learning 

solutions while also contributing to the end-to-end application stack—ranging from backend services to 

frontend interfaces. You will work cross-functionally with data scientists, software engineers, and 

product teams to build AI-driven applications that drive real business impact.

Key Responsibilities

Machine Learning & AI Development
Design, develop, and optimize machine learning models for predictive analytics, 
recommendation systems, NLP, or computer vision.
Implement, train, and fine-tune models using frameworks like TensorFlow, PyTorch, Scikitlearn, or Hugging Face.
Perform data preprocessing, feature engineering, and model evaluation to ensure highperformance ML systems.
Deploy and monitor ML models in production environments, ensuring scalability and reliability.
Full-Stack Development
Develop and maintain backend services (APIs, microservices) using Python (FastAPI, Flask, 
Django).
Build scalable, responsive front-end interfaces using React, Vue.js, or Angular.
Integrate ML models into web and cloud-based applications with seamless user interactions.
Ensure security, performance, and maintainability of the full-stack architecture.
DevOps & MLOps
Automate deployment pipelines with CI/CD tools (Docker, Kubernetes, GitHub Actions, Jenkins).
Implement MLOps best practices for model versioning, retraining, and monitoring.
Solid understanding for how to optimize cloud infrastructure on AWS, Azure, or GCP for ML 
workloads.
Collaboration & Leadership
Work closely with data scientists, product managers, and software engineers to align ML 
solutions with business objectives.
Advocate for best practices in software engineering, ML model lifecycle management, and 
scalable architectures.
Contribute to code reviews, technical documentation, and knowledge-sharing initiatives.