Applied ML / MLOps Engineer

About the role We are looking for an Applied ML / MLOps Engineer to build AI-powered product features and the backend systems that support them. You will take solutions from problem definition and prototyping through evaluation, deployment, and ongoing improvement. This role combines applied AI, software engineering, and MLOps. You will collaborate with product, engineering, and QA teams to deliver accurate, reliable applications and own their behavior in production. Responsibilities - Translate product requirements into technical designs and measurable success criteria. - Build AI workflows for information extraction, classification, pattern detection, and recommendations. - Select models, design prompts and context, and integrate model outputs into application features. - Develop backend services, APIs, and data-processing workflows. - Build evaluation pipelines and regression tests to measure and improve AI quality. - Automate deployment, versioning, monitoring, and recovery. - Investigate bugs across data, models, application logic, and infrastructure. - Improve existing features and optimize accuracy, reliability, latency, and cost. - Own implementation, testing, release, and production support. Required expertise Applied AI and LLM engineering - Experience building and deploying AI-powered applications. - Model selection and comparison based on quality, latency, cost, and operational requirements. - Prompt engineering, context engineering, structured outputs, and schema validation. - Working knowledge of retrieval, embeddings, vector search, tool calling, and agent workflows. - Understanding of grounding, hallucinations, model limitations, and failure handling. - Ability to choose appropriate combinations of deterministic logic and AI. Evaluation and AI quality - Experience creating representative evaluation datasets and automated evaluation pipelines. - Ability to define task-specific metrics and acceptance criteria. - Systematic error analysis across inputs, prompts, models, and downstream processing. - Experience comparing model and prompt versions and detecting regressions. - Understanding of precision, recall, false positives, and false negatives. - Ability to use human review and production feedback to improve results. MLOps and production AI - Experience deploying and operating batch or online inference workflows. - Understanding of model, prompt, configuration, and data versioning. - Familiarity with experiment tracking, model registries, and artifact management. - Experience implementing CI/CD, release validation, controlled rollout, and rollback. - Monitoring of output quality, data quality, drift, failures, latency, throughput, and cost. - Reliable execution through retries, backoff, rate-limit handling, concurrency controls, and timeouts. - Ability to preserve traceability and reproduce results when investigating failures. Backend and distributed systems - Strong Python programming and maintainable software design. - Experience building production APIs with validation, authentication, authorization, and clear contracts. - Understanding of asynchronous processing, concurrency, state management, and caching. - Experience with event-driven architectures, messaging, and workflow orchestration. - Ability to reason about idempotency, partial failures, consistency, and recovery. - Experience reading, extending, and troubleshooting existing systems. Data engineering and analytics - Strong SQL and experience with relational databases and analytical data stores. - Data modeling, schema design, query optimization, and batch or streaming pipelines. - Handling of missing, duplicate, delayed, and out-of-order events. - Understanding of time-series processing, temporal joins, time zones, and reporting boundaries. - Ability to translate business definitions into accurate, testable calculations. - Experience with data validation, lineage, backfills, and historical reprocessing. Cloud infrastructure and operations - Experience building, deploying, and operating applications on a major cloud platform. - Practical knowledge of containers, managed compute, storage, messaging, and orchestration. - Familiarity with infrastructure as code, IAM, secrets management, and environment separation. - Experience with automated testing, CI/CD, logging, monitoring, and alerting. - Ability to diagnose production issues and balance reliability, scalability, and cost. Preferred experience - Google Cloud services such as Vertex AI, Cloud Run, Pub/Sub, BigQuery, Cloud SQL, Cloud Storage, and Workflows. - Gemini or comparable model APIs. - Django REST Framework or FastAPI; PostgreSQL and Redis. - Dataform, dbt, or comparable transformation frameworks. - Docker, Kubernetes, Terraform, and GitHub Actions or Cloud Build. - Vertex AI model lifecycle tools, MLflow, Kubeflow, or comparable tooling. - Multimodal or vision-language applications. - Operational analytics, live monitoring, timelines, or decision-support products. Comparable experience with AWS, Azure, and alternative frameworks is welcome. We value engineering depth, sound judgment, and production ownership. What success looks like - Delivers useful AI features with measurable quality. - Builds reliable services and consistent data flows. - Diagnoses problems systematically and verifies fixes. - Takes ownership throughout the feature lifecycle. Non-Negotiable Requirements • Working Hours: Consistent availability from 9:00 AM to 5:00 PM Pacific Time (PT), • Strong, clear technical communication during Pacific business hours.