AI/ML Engineer 6+yrs exp

( CA Locals Pref) 

Introduction:

The AI/ML Engineer with 6+ years of experience will play a crucial role in designing, building, and deploying large-scale machine learning systems for our Personal AI & Human Connection intelligence platform. This individual will work closely with product, data, and executive teams to develop impactful AI solutions for our users.

Responsibilities:

• Design and implement end-to-end ML pipelines including data ingestion, preprocessing, model training, validation, and deployment.
• Develop and productionize ML models for prediction, classification, and recommendation tasks.
• Build automated retraining and evaluation pipelines using tools such as AWS SageMaker, Vertex AI, Kubeflow, or MLflow.
• Develop and maintain feature stores and data transformation pipelines using Spark, PyTorch, or TensorFlow.
• Deploy and serve models via REST/gRPC endpoints or Lambda-based inference APIs with high availability and low latency.
• Collaborate with backend engineers to integrate models into production microservices and event-driven workflows.
• Implement model observability and drift detection, ensuring compliance with security, privacy, and regulatory standards.
• Write clean, testable code and create unit, integration, and performance tests for critical ML components.
• Partner with product and data science teams to refine model goals, define success metrics, and optimize inference performance.

Requirements:

• Bachelor''s or Master''s degree in Computer Science, Machine Learning, or related field.
• 6+ years of hands-on experience building, training, and deploying ML models in production.
• Expertise in Python and ML libraries/frameworks such as PyTorch, TensorFlow, and scikit-learn.
• Strong experience with AWS AI/ML stack (S3, SageMaker, Lambda, Step Functions, ECS, DynamoDB, RDS).
• Proficiency in data pipelines (Airflow, Spark, Glue) and feature engineering for large, structured/unstructured datasets.
• Familiarity with Docker/Kubernetes and deploying ML services via CI/CD.
• Deep understanding of ML lifecycle management and versioning tools like MLflow, DVC, Weights & Biases.
• Knowledge of prompt engineering, RAG pipelines, and LLM integration is a plus.
• Experience handling sensitive data (PII) with a focus on security, encryption, and compliance.
• Excellent debugging and optimization skills across model performance, latency, and infrastructure cost.