Machine Learning Engineer II

Job Overview:
We are seeking a skilled AI/ML Developer with hands-on experience in building and deploying machine learning models using frameworks like Django, FastAPI, and Flask. In this role, you will work on integrating AI/ML models into scalable, production-ready applications and services, leveraging cloud platforms (AWS, Azure, or Google Cloud). You should have a solid understanding of ML algorithms, experience with backend development, and the ability to deploy and monitor models in a cloud environment.

Key Responsibilities:
Model Development and Integration:

Design, build, and train machine learning models for various use cases, such as classification, regression, and natural language processing.
Integrate machine learning models into backend services using Django, FastAPI, or Flask frameworks.
API Development:

Create RESTful APIs and microservices to expose machine learning models, allowing applications to interact with AI-driven solutions in real-time.
Design robust API endpoints for prediction and model management using FastAPI or Flask for performance and scalability.
Data Preprocessing and Feature Engineering:

Work with large datasets to preprocess, clean, and structure data for optimal performance in ML models.
Implement pipelines for data processing, including data ingestion, validation, and feature extraction.
Model Deployment and Cloud Integration:

Deploy machine learning models in production environments, using cloud platforms like AWS, Google Cloud, or Azure.
Utilize cloud services such as AWS SageMaker, Google AI Platform, or Azure ML for model training, deployment, and monitoring.
Optimize models for scalability, efficiency, and performance within cloud infrastructure.
Performance Monitoring and Tuning:

Monitor deployed models to ensure performance consistency and scalability. Use logging, alerting, and monitoring tools (e.g., Prometheus, Grafana).
Retrain models and optimize API performance based on feedback and changing requirements.
Collaboration and Agile Development:

Work closely with software engineers, data scientists, and DevOps teams to build AI-driven features and services.
Participate in an Agile development process, contributing to sprint planning, code reviews, and testing.
Security and Compliance:

Ensure that machine learning models and APIs comply with industry standards and security best practices.
Implement data privacy protocols to comply with regulations such as GDPR and HIPAA.
Required Skills & Experience:
Frameworks and Tools:

Proficiency in Python with hands-on experience in frameworks like Django, FastAPI, and Flask for backend development.
Familiarity with ML libraries such as TensorFlow, PyTorch, Scikit-learn, and Keras.
API Development:

Experience building and deploying RESTful APIs with Django, FastAPI, or Flask to serve ML models.
Knowledge of JSON, OpenAPI (Swagger), and asynchronous programming with FastAPI.
Cloud Services:

Strong experience with cloud platforms such as AWS, Google Cloud, or Azure for model deployment and orchestration.
Familiarity with cloud-based AI/ML services like AWS SageMaker, Azure ML Studio, or Google AI Platform.
Model Deployment and Monitoring:

Experience with containerization and orchestration tools such as Docker and Kubernetes for deploying scalable AI/ML solutions.
Familiarity with CI/CD pipelines for automating the deployment and retraining of machine learning models.
Data Engineering:

Solid understanding of data preprocessing, feature engineering, and ETL pipelines.
Experience working with databases like PostgreSQL, MySQL, or MongoDB.
Version Control:

Experience with version control tools like Git and collaborative platforms like GitHub or GitLab.
Preferred Qualifications:
Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or related fields.
Certifications: Cloud certifications (e.g., AWS Certified Machine Learning, Google Professional Machine Learning Engineer, or Azure AI Engineer Associate).
Experience: 2+ years of professional experience in AI/ML development and cloud services.
Knowledge of CI/CD principles and DevOps tools for continuous integration and delivery.
Soft Skills:
Problem-Solving: Ability to troubleshoot and solve complex issues related to machine learning deployment and backend services.
Team Collaboration: Excellent communication skills and ability to work in cross-functional teams.
Adaptability: Eagerness to stay up-to-date with the latest trends in AI/ML and backend development technologies.