Software Engineer-AI/ML

Job Description

Role Overview:
As an AI/ML Engineer, your primary responsibility will be to build, deploy, and scale Python applications along with AI/ML solutions. You will work closely with clients and software engineers to implement machine learning models and deploy them into production environments. Your expertise in Python programming and AI/ML frameworks will be crucial in developing intelligent solutions tailored to specific business requirements.

Key Responsibilities:
– Expertise in Python, with a deep understanding of Flask/FastAPI.
– Proficient in server programming to implement complex business logic effectively.
– Understanding of fundamental design principles for scalable applications.
– Design, develop, and deploy machine learning models and AI algorithms independently.
– Solve technical challenges by researching and implementing innovative AI/ML solutions.
– Build and maintain integration (e.g., APIs) for exposing machine learning models and facilitating system integration.
– Perform data preprocessing, feature engineering, and optimization of datasets for model training and inference.
– Implement, monitor, and 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 for smooth communication between APIs and other systems, troubleshooting integration issues.
– 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, actively contributing to the team’s knowledge base.

Qualifications Required:
– Proficiency in Python (mandatory), R, or similar languages commonly used in ML/AI development.
– Hands-on experience with TensorFlow, PyTorch, scikit-learn, or similar ML libraries.
– Strong knowledge of data preprocessing, data cleaning, and feature engineering.
– Familiarity with model deployment best practices using Docker, Kubernetes, or cloud platforms (AWS, Azure, Google Cloud).
– Strong understanding of statistical methods, probability, and data-driven decision-making processes.
– Proficient in querying databases to extract relevant data for ML projects.
– Experience with ML lifecycle management tools such as MLflow, Kubeflow.
– Experience with NLP frameworks (e.g., spaCy, NLTK, BERT) for language-based AI solutions.
– Familiarity with image processing and computer vision techniques.
– Experience with managed ML services like AWS SageMaker, Azure Machine Learning, or Google Cloud AI Platform.
– Familiarity with agile workflows, including experience working with DevOps or CI/CD pipelines.

Additional Company Details: (Omitted as none provided in the JD),