Data Scientist

• Collaborate with team to design, develop, and deploy robust data science and AI solutions with a focus on generative AI (GenAI) models and frameworks (e.g., LLMs, diffusion models).
• Optimize and cloud-based infrastructure (AWS preferred Good to have) for scalable, reliable, and secure data processing and model deployment.
• Develop and maintain data pipelines, ETL processes, and model training workflows.
• Build and implement CI/CD pipelines for machine learning models and data applications (e.g., using Jenkins, GitHub Actions, etc.).
• Optimize data storage solutions (e.g., data lakes, SQL/NoSQL databases) for performance and scalability.
• Collaborate with cross-functional teams, including product, engineering, and business stakeholders, to deliver impactful AI features and insights.
• Monitor model and system performance, troubleshoot issues, and continuously improve deployment workflows.
• Stay abreast of advancements in GenAI, MLOps, data science, and cloud technologies, bringing fresh ideas to the team.
• Contribute to technical documentation and participate in code and design reviews.

You have got what it takes if you have

• 2+ years of hands-on experience in data science, machine learning engineering, or GenAI engineering roles.
  Proficiency with at least one programming language used in data science (Python preferred; experience with Java is a plus).
• Experience in developing and deploying machine learning, deep learning, or Generative AI models (e.g., LLMs, Transformers, diffusion models).
• Exposure to AWS services (e.g., S3, EC2, Lambda, SageMaker, RDS) or similar cloud platforms.
• Hands-on experience building CI/CD pipelines (e.g., with Jenkins) for machine learning and data applications.
• Solid background in SQL and/or NoSQL databases, with experience in data modeling and performance tuning.
• Strong problem-solving skills and the ability to think critically and creatively about data and AI-driven challenges.
• Excellent collaboration and communication skills, with a proven ability to work on cross-functional teams.

An extra dose of awesome if you have

• Familiarity with front-end frameworks for AI-powered applications (e.g., React, Streamlit, Gradio).
• Experience with containerization (e.g., Docker) and orchestration (e.g., Kubernetes) for machine learning deployments.
• Experience working in Agile environments.
• Prior exposure to MLOps tools, MLflow, Airflow, or similar workflow orchestrators.