ML Platform Engineer, Salesforce

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, or related field
• 5+ years of experience in data engineering, platform engineering, or MLOps roles
• Strong proficiency in Python and familiarity with distributed data frameworks such as Airflow, Spark or Flink
• Hands-on experience with feature store technologies (e.g., Feast, SageMaker Feature Store, Tecton, Databricks Feature Store, or custom implementations)
• Experience with cloud data warehouse (e.g., snowflake) and transformation framework (e.g. dbt) for data modeling, transformation and feature computation in batch environment
• Expertise in streaming data platforms (e.g., Kafka, Kinesis, Flink) and real-time data processing architectures
• Experience with cloud environments (AWS preferred) and infrastructure-as-code tools (Terraform, CloudFormation)
• Strong understanding of CI/CD automation, containerization (Docker, Kubernetes), and API-driven integration patterns
• Knowledge of data governance, lineage tracking, and feature lifecycle management best practices
• Excellent communication skills, a collaborative mindset, and a strong sense of ownership

Desirable

• We are seeking a highly skilled and motivated AI Platform Engineer with a focus on Feature Store development and management to join our growing AI/ML platform team
• . In this role, you will design, build, and scale the data and infrastructure components that power our machine learning ecosystem – enabling consistent, reliable, and real-time access to features across development, training, and production environments
• You’ll collaborate closely with data scientists, ML engineers, and data platform teams to streamline feature engineering workflows and ensure seamless integration between offline and online data sources
• You’ll be expected to work across multiple domains including data architecture, distributed systems, software engineering, and MLOps
• You will help define and implement best practices for feature registration, drift, governance, lineage tracking, and versioning, all while contributing to the CI/CD automation that supports feature deployment across environments
• Feature Store Design & Development: Architect, implement, and maintain a scalable feature store serving offline (batch), online (real-time), and streaming ML use cases
• Ecosystem Integration: Build robust integrations between the feature store and ML ecosystem components such as data pipelines, model training workflows, model registry, and model serving infrastructure
• Streaming & Real-Time Data Processing: Design and manage streaming pipelines using technologies like Kafka, Kinesis, or Flink to enable low-latency feature generation and real-time inference
• Feature Governance & Lineage: Define and enforce governance standards for feature registration, metadata management, lineage tracking, and versioning to ensure data consistency and reusability
• Collaboration with ML Teams: Partner with data scientists and ML engineers to streamline feature discovery, definition, and deployment workflows, ensuring reproducibility and efficient model iteration
• Data Pipeline Engineering: Build and optimize ingestion and transformation pipelines that handle large-scale data while maintaining accuracy, reliability, and freshness
• CI/CD Automation: Implement CI/CD workflows and infrastructure-as-code to automate feature store provisioning and feature promotion across environments (Dev → QA → Prod)
• Cloud Infrastructure & Security: Collaborate with platform and DevOps teams to ensure secure, scalable, and cost-effective operation of feature store and streaming infrastructure in cloud environments
• Monitoring & Observability: Develop monitoring and alerting frameworks to track feature data quality, latency, and freshness across offline, online, and streaming systems