Data Engineer

Roles & Responsibilities:

1. Model Scoring & Scaling: Develop scalable model scoring functions that handle high-volume, low-latency AI requests, ensuring models perform effectively across batch and real-time use cases.
2. Scalable Data Access Patterns: Design and implement robust data access patterns for both batch and real-time AI processes, enabling optimal performance for traditional machine learning and generative AI models.
3. Cloud Integration: Deploy AI models on cloud platforms, ensuring they are scalable, secure, and cost-efficient, supporting both batch and real-time workloads.
4. CICD Pipeline Implementation: Build and maintain CICD pipelines for the continuous integration, testing, and deployment of AI models and APIs, ensuring a streamlined and automated development process.
5. Problem Solving & Optimization: Collaborate with cross-functional teams to solve complex business challenges through the integration of scalable AI solutions, improving business outcomes.
6. Collaboration & Coordination: Work with data scientists, engineers, and business stakeholders to ensure AI models are successfully integrated and aligned with business goals.
7. Security & Compliance: Ensure that all AI models and solutions meet internal security requirements and comply with industry regulations, particularly in the banking sector.

Essential Skills:

1. 3 to 7 years of Experience.
2. Proficiency in Python, R, Spark ETL, Teradata, AWS tools (Sage maker, EMR, Redshift), Snowflake, with experience maintaining scalable ML models.
3. Hands-on experience with cloud platforms (AWS, GCP, Azure) for AI deployment and integration, supporting both batch and real-time data processing.
4. Expertise in scalable data access patterns for both batch and real-time AI models.
5. Experience designing, building, and maintaining in Data Engineering solutions
6. Experience setting up CICD pipelines for automating the deployment and testing of AI models.
7. Strong problem-solving skills, with a focus on creating scalable solutions that deliver business impact.

Preferred Qualification:

1. Experience in the banking or financial services industry, particularly with AI integration projects.
2. Familiarity with data pipeline and processing tools like Apache Kafka, Spark, or similar.
3. Utilize containerization technologies such as Docker and Kubernetes for scalable, secure model deployment and orchestration in cloud environments.
4. Knowledge of AI governance, risk management, and compliance frameworks in regulated environments.

Education Qualifications:

1. B.E. or B. Tech. or equivalent or post-graduation degree in Computer Applications or IT or Business Administration with 10+ years of relevant experience.