Design and develop Generative AI solutions using cloud-based managed AI services (e.g. Amazon Bedrock, Amazon SageMaker, Google Cloud Vertex AI).
Design and develop AI Agent workflows using cloud and open source agentic frameworks.
Containerize AI applications and deploy them using cloud orchestration services.
Collaborate with data architects/engineers to build end-to-end AI pipelines.
Implement MLOps practices to automate the development, deployment, and monitoring of AI applications and models.
Implement and manage robust monitoring systems for AI solutions in production environments, ensuring continuous performance tracking, anomaly detection, and model drift analysis; collaborate with cross-functional teams to deploy model updates, maintain version control, and optimize model efficiency over time.
Use Infrastructure as Code (IaC) to manage and version cloud resources for AI projects.
Ensure clear and accessible knowledge transfer to internal teams and create knowledge-sharing resources to ensure smooth transitions during model handoffs and system updates.
Contribute to the development of best practices and standards for AI engineering within the organization.
Requirements
Bachelor’s degree and 6 years’ experience in Architect/Engineer roles OR a Master’s degree and 4 years’ experience Architect/Engineer roles OR a Ph.D. and 1 years’ experience in Architect/Engineer roles OR 10 years’ experience in Architect/Engineer roles.
Experience using Python programming language to create and integrate GenAI solutions related to business workloads
2 years of experience of managing AI services within one cloud platform (AWS preferred, Google Cloud, Azure)
Experience with container services and orchestration (AWS EKS, ECS, GKE, etc.)
Experience in common machine learning, deep learning, and LLM frameworks, such as TensorFlow, PyTorch, scikit-learn, Hugging Face Transformers, LangChain, LangGraph.
2 years experience with using Terraform for managing Infrastructure as Code (IaC) as well as CI/CD deployments.
Experience in a client-facing role (preferred)
In-depth knowledge of data services across major cloud platforms (AWS or Google Cloud) (preferred)
Professional certifications with focus on AI/ML from AWS, Google Cloud or Azure (preferred)
Experience with agentic workflows and streaming data processing (preferred)
Travel 15% of the time
Applicants must currently be authorized to work in the United States for any employer without current or future sponsorship.
Benefits
the flexibility to take as much vacation with pay as they deem consistent with their duties, the company’s needs, and its obligations
seven paid holidays throughout the calendar year
up to 160 hours of paid wellness annually for their own wellness or that of family members
additional paid time off in the form of bereavement leave, time off to vote, jury duty leave, volunteer time off, military leave, and parental leave
health care insurance (medical, dental, vision)
retirement planning (401(k))
paid days off (sick leave, parental leave, flexible vacation/wellness days, and/or PTO)
Flexible Work Option: Can work remotely anywhere in the specified country
Position may be eligible for additional compensation that may include an incentive program
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
PythonGenerative AIMLOpsInfrastructure as CodeTerraformmachine learningdeep learningLLM frameworkscontainerizationcloud orchestration
Soft skills
collaborationknowledge transfercommunicationclient-facing experiencebest practices development
Certifications
AI/ML certifications from AWSAI/ML certifications from Google CloudAI/ML certifications from Azure