Talworx is an emerging recruitment consulting and services firm , we are hiring for our Fintech Product based client, In this role, you will be working with a team or teams of enthusiastic members supporting our critical technology systems and guiding our business partners & end users with industry best practices, solution design, and creating long term value to our customers.
engaging work environment. You will work in a (truly) global team and encouraged for thoughtful risk-taking and self-initiative.
Requirements
Job Summary:
As a member of the Cognitive Engineering team, you will build and maintain enterprise-scale data extraction, automation, and ML model deployment pipelines. You will design resilient, production-ready systems within an AWS-based ecosystem, collaborating with a hands-on, technically strong global team to solve high-complexity problems end-to-end.
Key Responsibilities:
- Develop, deploy, and operate data extraction and automation pipelines in production.
- Integrate and deploy machine learning models into pipelines (e.g., inference services, batch scoring).
- Lead the delivery of complex extraction, transformation, and ML deployment projects.
- Scale pipelines on AWS (EKS, ECS, Lambda) and manage DataOps processes with Celery, Redis, and Airflow.
- Implement robust CI/CD pipelines on Azure DevOps and maintain comprehensive test coverage.
- Strengthen data quality and reliability through logging, metrics, and automated alerts.
- Partner with data scientists, ML engineers, and product teams to align on requirements and delivery timelines.
Technical Requirements:
- 2-6 years of relevant experience in data engineering, automation, or ML deployment.
- Expert proficiency in Python, including building extraction libraries and RESTful APIs.
- Hands-on experience with task queues and orchestration: Celery, Redis, Airflow.
- Strong AWS expertise: EKS/ECS, Lambda, S3, RDS/DynamoDB.
- Containerization and orchestration experience: Docker (mandatory), basic Kubernetes (preferred).
- Proven experience deploying ML models to production.
- Solid understanding of CI/CD practices and hands-on experience with Azure DevOps.
- Familiarity with SQL and NoSQL stores (e.g., PostgreSQL, MongoDB)
