Senior Data & ML Engineer (Python & AWS)

Project Description:

Join a dynamic, cutting-edge project in the [finance/pharma/technology] sector focused on leveraging advanced data science, machine learning, and cloud-native solutions. The project aims to implement scalable AI/ML pipelines, optimize data-driven decision-making, and deliver actionable insights through state-of-the-art data engineering and MLOps practices. You will work closely with cross-functional teams to design, develop, and deploy AI solutions that have a direct impact on business outcomes.

Responsibilities:

• Apply statistical modeling and machine learning techniques to solve complex business challenges.

• Conduct exploratory and confirmatory data analysis, ensuring high-quality data preprocessing and preparation.

• Develop and implement Time Series Forecasting, Anomaly Detection, NLP, Generative AI, and Deep Learning solutions.

• Design, build, and maintain end-to-end data pipelines and data warehousing solutions.

• Implement MLOps/DevOps best practices, including CI/CD pipelines, containerization (Docker), and orchestration with cloud-native Kubernetes services (e.g., Amazon EKS, GKE, AKS).

• Automate infrastructure using Terraform, AWS CloudFormation, or AWS CDK.

• Design scalable and robust solution architectures to support AI/ML initiatives.

• Create interactive dashboards and visualizations using tools such as Streamlit, Posit, or Tableau.

• Collaborate in Agile frameworks (SCRUM, Kanban) to deliver iterative improvements.

• Communicate technical concepts effectively to both technical and non-technical stakeholders.

Qualifications & Requirements:

Substantial hands-on experience applying statistical modeling and ML to complex business problems.

Expertise in exploratory and confirmatory data analysis, statistics, probability, and optimization.

Proficiency in Python; experience with Kedro is highly desirable.

Applied experience with Time Series Forecasting, Anomaly Detection, NLP, Generative AI, and Deep Learning.

Proven experience with cloud infrastructure and services (AWS).

Strong understanding of MLOps and DevOps principles.

Containerization experience (Docker) and cloud-native Kubernetes services.

Proficiency in CI/CD, automation, and infrastructure as code.

Experience designing and developing data warehouses (e.g., Snowflake).

Strong SQL development skills and ability to build end-to-end data pipelines.

Expertise in solution architecture design.