Senior AI Engineer

We are looking for a Senior AI Engineer to serve as a key individual contributor, responsible for designing, building, and deploying machine learning systems that directly impact our core product capabilities. You will be integral to the implementation, model optimization, and reliability of our production AI services. This role is perfect for a hands-on engineer who thrives on solving complex technical challenges and driving projects autonomously from conception through deployment, making a tangible difference with every line of code. This position is fully remote This role is an Individual Contributor A day in the life as a Senior AI Engineer... Design, develop, train, and fine-tune complex ML models (deep learning and classical techniques) to solve high-priority business problems, with deployment targeted primarily on Google Cloud Platform Own end-to-end model deployment on GCP (Vertex AI, GKE, Cloud Run), ensuring performance, scalability, and stability under low-latency production requirements Build and maintain MLOps pipelines on GCP (Vertex AI Pipelines, Cloud Build, Artifact Registry) for automated training, testing, versioning, and CI/CD Design and build agentic AI systems and multi-agent workflows using frameworks such as Google ADK, LangChain, LlamaIndex, or AutoGen, integrated with GCP services (Vertex AI, Gemini models) Write clean, well-tested, production-grade Python and C# code; participate actively in code reviews to uphold engineering standards. Profile and optimize training and inference speed and cost, particularly for large datasets and distributed/constrained environments on GCP infrastructure Author technical user stories covering the full ML development lifecycle Actively participate in and help drive team ceremonies, sprint planning, and continuous process improvement Partner with Data Engineering to define data infrastructure, features, and pipelines (BigQuery, Dataflow, Pub/Sub) needed for training and serving Partner with DevOps and Cloud teams to build reliable, cost-optimized ML solutions on GCP Collaborate continuously with product owners and stakeholders to refine technical solutions and roadmaps within an agile framework Implement monitoring dashboards (Vertex AI Model Monitoring, Cloud Monitoring) to track drift, accuracy, latency, and cost, addressing issues proactively Proactively identify, develop, and validate new features to improve model performance and generalization Mentor engineers on ML and GCP best practices, and provide technical leadership on architecture decisions. What you'll bring to the table... Strong hands-on experience with Google Cloud Platform for ML: Vertex AI (Training, Pipelines, Model Registry, Endpoints, Model Monitoring), BigQuery, Cloud Run, GKE, and Cloud Build Strong proficiency in Python and C#, with deep experience in core ML frameworks (TensorFlow, PyTorch, scikit-learn) Strong knowledge of Agentic AI frameworks: Google ADK, AutoGen, LangChain, LlamaIndex, and experience integrating with Gemini/Vertex AI foundation models Strong understanding of distributed training, model serving architecture, and best practices for scaling ML applications on GCP Hands-on experience with MLOps tools (Vertex AI Pipelines, MLflow, DVC, Kubeflow) and containerization (Docker, Kubernetes/GKE) Direct experience building and deploying ML solutions on Google Cloud (Vertex AI required); familiarity with AWS SageMaker or Azure ML a plus Solid theoretical foundation in machine learning, statistics, and optimization techniques Proficient in SQL (BigQuery), Pandas, and large-scale data processing (Dataflow/Apache Beam, Spark) Deep understanding of agile methodologies Strong communication, collaboration, and technical leadership skills Proven ability to mentor and guide other engineers Strong software engineering fundamentals: coding standards, code reviews, source control, testing, and operations Excellent problem-solving and cross-functional communication skills We'd love to hear from you if you have... Bachelor's degree in Computer Science, Software Engineering, Information Systems, or a related field (or equivalent practical experience) 5+ years of experience in machine learning engineering Proven track record of successfully designing, implementing, and deploying at least 2-3 significant ML models into a high-availability production system Crate&Barrel