Design and implement end-to-end ML architectures and scalable Java backend systems, and create cross-platform solution architectures. Lead model lifecycle, MLOps standards, cloud-native deployments, microservices design, integrations, CI/CD, and stakeholder alignment across product, data engineering, and infrastructure teams.
This is a remote position.
1. Job Title: AI/ML Architect
Key Responsibilities: Design and review machine learning architecture pipelines end-to-end. Architect scalable models using tools like TensorFlow, PyTorch, Scikit-learn. Drive data strategy, feature stores, model lifecycle (training, deployment, monitoring). Collaborate with data engineering and product teams to align technical goals. Define standards for MLOps, model governance, and responsible AI.
Must-Have Skills: 8+ years in ML/DL, data science, or AI-related roles. Expertise in Python, ML frameworks, distributed computing (Spark, Dask). Cloud-native ML deployment on AWS SageMaker, Azure ML, GCP Vertex AI. Knowledge of Kubeflow, MLFlow, Airflow, Docker/K8s for ML pipelines. Strong grasp of ML lifecycle management, monitoring, and retraining.
2. Job Title: Java Architect
Key Responsibilities: Own the technical design for backend systems built on Java / Spring Boot. Create solution blueprints, architecture documents, and design patterns. Guide teams on microservices, containerization, API design, and DevOps. Conduct code reviews, architecture assessments, and risk mitigation. Collaborate with product and infrastructure teams for seamless delivery.
Must-Have Skills: Proficiency in Java 11+/Spring Boot, RESTful APIs, ORM frameworks. Experience with Kubernetes, Docker, messaging systems (Kafka, RabbitMQ). Strong grasp of cloud-native development (AWS, Azure, GCP). In-depth understanding of microservices, DDD, clean architecture. Performance tuning, scalability, and reliability design.
3. Job Title: Solution Architect
Key Responsibilities: Analyze requirements and design technical solutions across platforms. Create architecture roadmaps for applications, integrations, and cloud infrastructure. Lead POCs, architecture evaluations, and tech selection. Align solutions with enterprise architecture standards and security best practices. Act as a technical bridge between stakeholders, developers, and leadership.
Must-Have Skills: Strong experience in application architecture, APIs, and system integration. Familiarity with cloud platforms (Azure, AWS, GCP). Solid understanding of data architecture, DevOps practices, CI/CD pipelines. Experience with design patterns, scalability, and resilience planning. Great communication, documentation, and stakeholder management skills.
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What you need to know about the Los Angeles Tech Scene
Los Angeles is a global leader in entertainment, so it’s no surprise that many of the biggest players in streaming, digital media and game development call the city home. But the city boasts plenty of non-entertainment innovation as well, with tech companies spanning verticals like AI, fintech, e-commerce and biotech. With major universities like Caltech, UCLA, USC and the nearby UC Irvine, the city has a steady supply of top-flight tech and engineering talent — not counting the graduates flocking to Los Angeles from across the world to enjoy its beaches, culture and year-round temperate climate.
Key Facts About Los Angeles Tech
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