Technical Architect – ML

This is a fully remote position, open to applicants in United States.

📋 Description

• Design and implement the MLOps strategy for the program, ensuring alignment with the project proposal and delivery roadmap.

• Develop and manage enterprise-level ML/LLM pipelines that encompass model training, validation, deployment, versioning, monitoring, and CI/CD automation.

• Construct container-oriented ML platforms with a focus on EKS while assessing alternative orchestration tools.

• Establish hybrid MLOps and LLMOps workflows, which include prompt/version governance, evaluation frameworks, and monitoring for LLM-based systems.

• Act as a technical authority on both internal and customer projects by providing architectural patterns, best practices, and reusable frameworks.

• Facilitate observability, monitoring, drift detection, lineage tracking, and auditability across ML/LLM systems.

• Set and enforce standards for model deployment, monitoring, governance, and automation.

• Work collaboratively with data engineering, platform, DevOps, and client stakeholders to deliver production-ready ML solutions.

• Ensure that all solutions meet security, governance, and compliance standards.

• Conduct architecture reviews, troubleshoot intricate ML system issues, and guide implementation on cloud-native ML platforms.

• Mentor engineers on contemporary MLOps tools, platform capabilities, and best practices.

⛳️ Requirements

• A minimum of 8 years of experience in ML/AI engineering or MLOps roles with significant architectural exposure.

• Extensive proficiency in the AWS cloud-native ML stack, including SageMaker, EKS, Lambda, API Gateway, and CI/CD tools such as CodeBuild or CodePipeline.

• Practical experience with at least one major MLOps toolset, along with familiarity with alternatives such as MLflow, Kubeflow, SageMaker Pipelines, Airflow, BentoML, KServe, and Seldon.

• Profound understanding of model lifecycle management, covering feature engineering, training, registry, deployment, and monitoring.

• Experience in implementing or supporting LLMOps pipelines, which include prompt versioning, evaluation metrics, and automation frameworks.

• Comprehensive knowledge of the entire ML lifecycle: data ingestion, feature engineering, training, evaluation, model packaging, CI/CD, drift detection, monitoring, and governance.

• Strong expertise with AWS SageMaker Pipelines, Feature Store, Model Registry, and Model Monitor.

• Experience in establishing ML CI/CD pipelines with automated training, testing, validation, model promotion, and endpoint deployment.

• Familiarity with Infrastructure as Code tools and CI/CD pipelines.

• Experience in Kubernetes-based development.

• Background in feature engineering pipelines and Feature Store management.

• Understanding of lineage tracking, which includes training data snapshots, feature versions, code versioning, metadata tracking, and reproducibility.

• Practical experience with AWS Bedrock and Agentcore service.

• Experience with CloudWatch, SageMaker Model Monitor, Prometheus, and Grafana.

• Strong foundation in Python and cloud-native development patterns.

• Solid understanding of security best practices, IAM, secrets management, and artifact governance.

• Nice to have: experience with vector databases, RAG pipelines, or multi-agent AI systems.

• Nice to have: familiarity with DevOps and infrastructure-as-code tools such as Terraform, Helm, and CDK.

• Nice to have: knowledge of model drift detection, A/B testing, canary rollouts, and blue-green deployments.

• Nice to have: experience with observability stacks including Prometheus, Grafana, CloudWatch, and OpenTelemetry.

• Nice to have: SQL and data transformation skills using Snowflake, Databricks, and Spark.

• Ability to translate business objectives into scalable AI/ML platform designs.

• Excellent communication and cross-team collaboration skills.

• Capability to guide engineering teams through technical ambiguity and design decisions.

🏝️ Benefits

• A culture founded on transparency, diversity, integrity, learning, and growth.

• Numerous opportunities to learn, develop, and engage with colleagues from diverse experiences and backgrounds worldwide.

• A hybrid work culture (company-wide description).