AI/ML Engineer

Job Title: AI/ML Engineer

Location: Redwood City,Ca /SanRamon,ca (Hybrid-3 Days Onsite)

Experience: 5+Years Required

Description:

Senior AI/ML Engineer Key Responsibilities

• Design and implement end-to-end ML pipelines, including data ingestion, preprocessing, model training, validation, and deployment.
• Build and productionize ML models for prediction, classification, and recommendation use cases.
• Create automated retraining and evaluation workflows using AWS SageMaker, Vertex AI, Kubeflow, or MLflow.
• Develop and maintain feature stores and transformation pipelines using Spark, PyTorch, or TensorFlow.
• Deploy and serve models via REST/gRPC services or Lambda-based inference APIs with high availability and low latency.
• Collaborate with backend engineers to integrate models into microservices and event-driven architectures.
• Implement model observability, monitoring, and drift detection, including accuracy, latency, bias, and cost metrics.
• Ensure ML systems adhere to security, privacy, and compliance frameworks (HIPAA, SOC 2).
• Write clean, testable code with comprehensive unit, integration, and performance tests.
• Work with product and data science teams to refine model objectives, success metrics, and inference optimization.

• Bachelor s/Master s in Computer Science, Machine Learning, or related field.
• 4+ years of experience building, training, and deploying ML models in production.
• Expertise in Python and ML frameworks such as PyTorch, TensorFlow, and scikit-learn.
• Strong hands-on experience with AWS ML stack (S3, SageMaker, Lambda, Step Functions, ECS, DynamoDB, RDS).
• Proficiency in building data pipelines using Airflow, Spark, or AWS Glue.
• Experience with Docker, Kubernetes, and CI/CD for deploying ML services.
• Deep understanding of ML lifecycle management, versioning, and experiment tracking (MLflow, DVC, Weights & Biases).
• Knowledge of prompt engineering, RAG workflows, and LLM integrations is a plus.
• Experience working with sensitive/PII data, ensuring encryption, security, and compliance.
• Strong debugging and performance optimization skills across models, latency, and infrastructure cost.

• Experience building LLM-based applications using OpenAI, Anthropic, or Hugging Face APIs.
• Familiarity with vector databases (Pinecone, FAISS, Weaviate) and RAG implementations.
• Experience with MLOps workflow tools (SageMaker Pipelines, Vertex AI Pipelines, Kubeflow).
• Knowledge of data governance, ethical AI principles, and model explainability tools (SHAP, LIME).