3 Flask Api Jobs

1 Month to ASAP joinees required As a Developer, you will play a crucial role in designing robust architectures and ensuring the successful implementation of our software products. You will work with a team of developers and data engineers to leverage your expertise in Python, data engineering technologies, and cloud platforms to drive the development process. Additionally, you will work closely with a Technical Architect to understand and implement the solution architecture and debug issues in production. Responsibilities: Design and implement scalable, efficient, and maintainable software solutions. Deliver high-quality, well-documented, and maintainable code. Implement and manage thorough testing processes, including automated testing. Develop processes and practices to debug issues that arise in production environments. Implement best practices for coding standards, testing, and deployment. Develop and maintain data scraping/integration solutions to collect and process large volumes of data. Collaborate with product managers, machine learning engineers, and other stakeholders to understand requirements and deliver high-quality products. Work closely with the Technical Architect to understand the solution architecture and implement it effectively. Stay updated with the latest industry trends and technologies Required Skills and Experience: Education: Bachelor's or Master's degree in Computer Science, Engineering, or a related field. Experience: Minimum 4 years experience in software development. Knowledge of data privacy and security best practices. Familiarity with agile development methodologies. Have a good knowledge of architecture, design patterns, and advanced software development techniques Technical Skills: Python: Advanced proficiency in Python programming and experience with its libraries and frameworks such as Pandas, NumPy, Flask, Django, FastAPI etc. Data Scraping: Experience with data engineering tools and frameworks such as BeautifulSoup, Scrapy, Selenium, Playwright etc. Experience with data pipeline tools such as Airflow, kafka, Spark would be a plus AWS: Compute Services: Experience with AWS compute services such as EC2, Lambda, EKS and ECS. Storage Services: Experience with AWS storage services like S3 and EBS. Databases: Knowledge of AWS database services including RDS (e.g., PostgreSQL, MySQL) and MongoDB. Networking: Understanding of AWS networking services like VPC, Route 53, and CloudFront. Security: Experience with AWS Identity and Access Management (IAM), AWS Key Management Service (KMS), and other security services. Deployment and Management: Experience with CI/CD pipelines and tools like Jenkins, GitLab CI and Terraform/Terragrunt, Flux CD Monitoring and Logging: Familiarity with AWS CloudWatch and AWS CloudTrail, DataDog, PagerDuty Containerization: Experience with Docker and container orchestration tools like Kubernetes. Ability to create and manage Docker containers for deploying applications. Database Management: Experience working with SQL and database management systems such as PostgreSQL, MySQL, NoSQL databases like Mongodb, ElasticDB. Experience with graph databases would be an advantage API Development: Experience in designing and consuming RESTful APIs. Familiarity with API management and documentation tools such as Swagger or Postman. Worked with FastAPI and/or FlaskAPI. Experience with Async.io would be a plus Version Control: Proficiency with version control systems like Git, including branching, merging, and pull requests. Testing Automation: Experience in setting up and using automated testing tools and frameworks, such as Selenium, PyTest, or JUnit. Documentation: Ability to create and maintain detailed sequence diagrams and contribute to architectural documentation. Debugging: Worked on debugging issues that arise in production environments. Soft Skills: Problem-Solving: Strong analytical and problem-solving skills. Communication: Excellent verbal and written communication skills. Collaboration: Ability to work collaboratively with cross-functional teams. Adaptability: Ability to adapt to new technologies and methodologies quickly.

ML Ops Engineer Skills: Expert in Python programming with experience in libraries like NumPy, Pandas etc. Experience in API deployment using frameworks like FastAPI, Flask, Django, Tornado, bottle etc. Total of 3-7 years of experience in managing machine learning projects end-to-end, with last project focused on MLOps. Experience in supporting model builds and model deployment for IDE-based models and autoML tools, experiment tracking, model management, version tracking & model training (Dataiku, Datarobot, Kubeflow, MLflow, neptune.ai), model hyperparameter optimization, model evaluation, and explainability (SHAP, Tensorboard).ss Experience with one of container technologies (Docker, Kubernetes, EKS, ECS). Experience with multiple cloud providers (AWS, GCP, Azure, etc), preferred GCP. Experience with MLOps tools such as ModelDB, Kubeflow, Pachyderm, and Data Version Control (DVC). Monitoring Build & Production systems using automated monitoring and alarm tools. Knowledge of machine learning frameworks: TensorFlow, PyTorch, Keras, Scikit-Learn. MLOps Engineer Responsibilities: Deploying and operationalizing MLOps. Model evaluation and explainability. Model training and automated retraining. Model workflows from onboarding, operations to decommissioning. Model version tracking & governance. Data archival & version management. Model hyperparameter optimization (Good to have). Model and drift monitoring. Creating and using benchmarks, metrics, and monitoring to measure and improve services. Providing best practices and executing POC for automated and efficient model operations at scale. Designing and developing scalable MLOps frameworks to support models based on client requirements. Desirable Education: Deep quantitative/programming background with a degree (Bachelors, Master’s, or Ph.D.) in a highly analytical discipline, like Statistics, Economics, Computer Science, Mathematics, Operations Research, etc.

Role Summary The ML Ops Engineer will be responsible for designing building and maintaining the infrastructure and processes for deploying and managing machine learning models in production Responsibilities Understand and translate business and functional needs into machine learning problem statements Translate complex machine learning problem statements into specific deliverables and requirements Design and develop scalable solutions that leverage machine learning and deep learning models to meet enterprise requirements Translate machine learning algorithms into productionlevel code Collaborate with development teams to test and deploy machine learning models Monitor the performance of deployed models track data or concept drift and update or retrain models as needed Ensure adherence to performance standards and compliance with data security requirements Keep abreast with new tools algorithms and techniques in machine learning and work to implement them in the organization Proficient in Python scripting and familiarity with Python packaging (e.g. PyPI, pip, virtualenv) Hands on experience with ML workflow tools like MLflow, Kubeflow, MLRun, DVC, Airflow leveraging Python integrations. Automate model training, testing and deployment processes using CI/CD tools. Experience with cloud platform preferably GCP and their Python SDKs. Experience with SQL and Python-based ETL processes. Familiarity with data processing frameworks like Apache, Spark or Dask using PySpark or similar Python interfaces. Collaborate with development teams to test, optimize ML workflows and deploy/integrate machine learning models into applications. Design and develop scalable solutions that leverage machine learning and deep learning models to meet enterprise requirements Translate machine learning algorithms into production-level code Knowledge of version control systems (e.g. git) and collaborative coding practices. Monitor the performance of deployed models, track data or concept drift, and update or retrain models as needed Ensure adherence to performance standards and compliance with data security requirements Keep abreast with new tools, algorithms and techniques in machine learning and work to implement them in the organization Education A bachelors degree in computer science data science applied mathematics software engineering or related masters degree preferred Specialization in applied machine learning or machine learning infrastructure preferred Experience 5-7 years of experience in developing and deploying enterprise scale machine learning solutions in a software engineering adjacent field Experience developing and debugging in Python Exposure to architectural patterns of largescale software applications Experience with REST API development in Python (e.g. Flask, Fast API) for model serving. Experience in developing, trouble shooting and resolving issues related to ML systems in production. Understanding of DevOps principles and exposure to architectural patterns of large-scale software applications Required skills Knowledge of working on any Cloud Environment GCP preferred Proficiency in deploying machine learning algorithms as production ready API services Advanced programming skills with Python Ability to effectively communicate technical concepts and results to technical and business audiences in a comprehensive manner Ability to collaborate effectively across multiple teams and stakeholders including analytics teams development teams product management and operations Ability to work independently and in a fully remote environment Willingness and ability to stay up to date on new MLAI technologies and their potential impact on the company