Position Overview:   We are seeking a highly skilled Platform Engineer with expertise in building self-serve platforms that combine real-time ML deployment and advanced data engineering capabilities. This role requires a blend of cloud-native platform engineering, data pipeline development, and deployment expertise. The ideal candidate will have a strong background in designing data workflows and scalable infrastructure for ML pipelines while enabling seamless integrations and deployments.   Responsibilities: Platform Engineering
Design and build self-serve platforms that support real-time ML deployment and robust data engineering workflows.
Develop microservices-based solutions using Kubernetes and Docker for scalability, fault tolerance, and efficiency.
Create APIs and backend services using Python and FastAPI to manage and monitor ML workflows and data pipelines. Real-Time ML Deployment
Architect and implement platforms for real-time ML inference using tools like AWS SageMaker and Databricks.
Enable model versioning, monitoring, and lifecycle management with observability tools such as New Relic. Data Engineering
Build and optimize ETL/ELT pipelines for data preprocessing, transformation, and storage using PySpark and Pandas.
Develop and manage feature stores to ensure consistent, high-quality data for ML model training and deployment.
Design scalable, distributed data pipelines on platforms like AWS, integrating tools such as DynamoDB, PostgreSQL, MongoDB, and MariaDB.
Implement data lake and data warehouse solutions to support advanced analytics and ML workflows. CI/CD and Automation
Design and implement robust CI/CD pipelines using Jenkins, GitHub Actions, and other tools for automated deployments and testing.
Automate data validation and monitoring processes to ensure high-quality and consistent data workflows. Documentation and Collaboration
Create and maintain detailed technical documentation, including high-level and low-level architecture designs.
Collaborate with cross-functional teams to gather requirements and deliver solutions that align with business goals.
Participate in Agile processes such as sprint planning, daily standups, and retrospectives using tools like Jira.

 

Required Qualifications

5+ years of experience in platform engineering, DevOps, or data engineering roles.
Hands-on experience with real-time ML model deployment and data engineering workflows.Technical Skills
Strong expertise in Python and experience with Pandas, PySpark, and FastAPI.
Proficiency in container orchestration tools such as Kubernetes (K8s) and Docker.
Advanced knowledge of AWS services like SageMaker, Lambda, DynamoDB, EC2, and S3.
Proven experience building and optimizing distributed data pipelines using Databricks and PySpark.
Solid understanding of databases such as MongoDB, DynamoDB, MariaDB, and PostgreSQL.Proficiency with CI/CD tools like Jenkins, GitHub Actions, and related automation frameworks.
Hands-on experience with observability tools like New Relic for monitoring and troubleshooting.