Data Scientist

About the Role, We are seeking a Data Scientist with over 8 years of experience to lead and implement advanced AI/ML solutions across cloud and on-premise infrastructures.

The ideal candidate has strong technical expertise in machine learning, cloud services, and software engineering practices, complemented by a robust understanding of automation, data workflows, and modern DevOps techniques.

Key Responsibilities-

1. Model Development & Optimization: Design and implement machine learning models with a focus on performance, scalability, and adaptability to address real-world challenges. Monitor and mitigate model drift to maintain the accuracy and relevance of deployed solutions.

2. Cloud-Based Generative AI Solutions: Develop and deploy AI/ML applications using services like SageMaker, Bedrock, Code Whisperer, and Azure ARM. Optimize workflows for generative AI use cases on cloud platforms, ensuring seamless integration with existing systems.

3. Programming and Scripting: Write efficient code using Python, SQL, and Bash scripting for data analysis, model training, and deployment. Automate repetitive processes to improve operational efficiency.

4. Infrastructure & CI/CD: Build and manage robust Infrastructure as Code (IaC) solutions to automate cloud resource provisioning. Integrate and utilize CI/CD tools to streamline code delivery and deployment pipelines.

5. Testing and Monitoring: Implement automated testing frameworks such as Selenium and pytest for quality assurance. Develop systems for continuous monitoring of APIs, data pipelines, and models in production.

6. Data Management & APIs: Design and optimize databases, including NoSQL systems, for efficient data storage and retrieval. Build, maintain, and enhance RESTful APIs to enable seamless integration with external applications.

7. Collaboration and Documentation: Collaborate with cross-functional teams to understand business needs and translate them into technical solutions. Document workflows, architectures, and model outputs to ensure clarity and maintainability.

Required Technical Expertise:

Proficiency in Python, SQL, and Bash scripting for automation and analysis.

Experience with cloud-based AI services like AWS SageMaker, Azure ML, and others.

Hands-on knowledge of IaC tools (e.g., Terraform, CloudFormation).

Familiarity with CI/CD tools such as Jenkins, GitHub Actions, or CircleCI.

Strong understanding of databases, including NoSQL technologies (e.g., MongoDB, Cassandra).

Testing and Monitoring: Expertise in automated testing frameworks like Selenium and Pytest.

Experience implementing model drift monitoring to ensure consistent performance.

Certifications in cloud platforms (AWS, Azure, or GCP).

Experience deploying large-scale generative AI models.

Understanding of microservices architecture and containerization (e.g., Docker, Kubernetes).