Senior AI Engineer

14 days ago

Remote, United StatesSenior

Responsibilities

Design, develop, train, fine-tune, and optimize deep learning and classical machine learning models for high-priority business problems.
Own end-to-end model deployment on Google Cloud Platform using Vertex AI, GKE, and Cloud Run.
Build and maintain MLOps pipelines for automated training, testing, versioning, and deployment.
Design and build agentic AI systems and multi-agent workflows using Google ADK, LangChain, LlamaIndex, or AutoGen with Gemini and Vertex AI models.
Write production-grade Python and C# code and participate in code reviews.
Optimize training and inference performance and cost for large datasets and distributed environments.
Define data infrastructure, features, and pipelines with Data Engineering using BigQuery, Dataflow, and Pub/Sub.
Build monitoring dashboards to track model drift, accuracy, latency, and cost, and address production issues proactively.
Collaborate with product owners and stakeholders on technical solutions and roadmaps.
Mentor engineers on machine learning and Google Cloud best practices and provide technical leadership on architecture decisions.

Bachelor’s degree in Computer Science, Software Engineering, Information Systems, or a related field, or equivalent practical experience.
At least 5 years of experience in machine learning engineering.
Proven experience designing, implementing, and deploying 2–3 significant machine learning models into high-availability production systems.
Strong hands-on experience with Google Cloud Platform for machine learning, including Vertex AI, BigQuery, Cloud Run, GKE, and Cloud Build.
Strong proficiency in Python and C# and deep experience with TensorFlow, PyTorch, and scikit-learn.
Knowledge of agentic AI frameworks including Google ADK, AutoGen, LangChain, and LlamaIndex, with experience integrating Gemini and Vertex AI foundation models.
Understanding of distributed training, model-serving architecture, and scaling machine learning applications on Google Cloud.
Hands-on experience with Vertex AI Pipelines, MLflow, DVC, Kubeflow, Docker, and Kubernetes/GKE.
Proficiency in SQL, BigQuery, Pandas, and large-scale data processing with Dataflow, Apache Beam, or Spark.
Strong software engineering, testing, source-control, operations, communication, collaboration, mentoring, and technical leadership skills.
Familiarity with AWS SageMaker or Azure ML is preferred.

Fully remote work arrangement.
Individual-contributor role with opportunities to mentor engineers and provide technical leadership.
The company provides reasonable accommodations for individuals with disabilities and is an equal opportunity employer.

Apache BeamApache SparkC#DockerDVCGoogle BigQueryGoogle Cloud Platform KubernetesMLflowPandasPython PyTorchscikit-learnSQLTensorFlow