We are seeking a Python Engineer with hands-on experience in building, deploying, and scaling Python applications along with AI/ML solutions. The ideal candidate will have strong programming skills, a passion for developing intelligent solutions, and familiarity with the latest AI/ML frameworks and tools. This role will involve close collaboration with client and software engineers to implement machine learning models and deploy them into production environments.
Required Experience: 2-4 Years
Required Educational Qualifications: UG: BE/BTech or PG: ME/M-Tech /MCA/MSC-IT/ Data Science, AI, Machine Learning, or a related field.
Key Responsibilities:
Expert in Python, with expert-level knowledge of Flask/FastAPI.
Expert in server programming, you should be able to implement complex business logic.
Good understanding of fundamental design principles behind a scalable application
Independently design, develop, and deploy machine learning models and AI algorithms tailored to business requirements.
Solve complex technical challenges by researching and implementing innovative AI/ML solutions.
Build and maintain Integration (e.g. APIs) to expose machine learning models and support integration with other systems.
Conduct data preprocessing, feature engineering, and optimize datasets for model training and inference.
Implement, monitor, and continuously improve model performance in production environments, focusing on scalability and efficiency.
Manage model deployment, monitoring, and scaling using tools like Docker, Kubernetes, and cloud services.
Develop integration strategies to ensure smooth communication between APIs and other systems, and troubleshoot integration issues as they arise.
Create and maintain comprehensive documentation for AI/ML projects, covering model parameters, API endpoints, and integration workflows.
Stay updated with emerging trends and technologies in AI/ML and actively contribute to the team’s knowledge base.
Must-Have Skills:
Programming Skills: Proficiency in Python (must have), R, or similar languages commonly used in ML/AI development.
Machine Learning Frameworks: Hands-on experience with TensorFlow, PyTorch, scikit-learn, or similar ML libraries.
Data Handling: Strong knowledge of data preprocessing, data cleaning, and feature engineering.
Model Deployment: Familiarity with model deployment practices, including using Docker, Kubernetes, or cloud platforms (AWS, Azure, Google Cloud).
Statistical Knowledge: Strong understanding of statistical methods, probability, and data-driven decision-making processes.
SQL and NoSQL Databases: Proficient in querying databases to extract relevant data for ML projects.
MLOps Tools: Experience with ML lifecycle management tools such as MLflow, Kubeflow.
Natural Language Processing (NLP): Experience with NLP frameworks (e.g., spaCy, NLTK, BERT) for language-based AI solutions.
Computer Vision: Familiarity with image processing and computer vision techniques.
Cloud ML Services: Experience with managed ML services like AWS SageMaker, Azure Machine Learning, or Google Cloud AI Platform.
Experience with Agile Development: Familiarity with agile workflows, including experience working with DevOps or CI/CD pipelines.
Good to Have Skills:
Big Data Technologies: Exposure to Spark, Hadoop, or similar big data processing tools.