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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Technical Architect - ML - **Company:** Quantiphi, Inc. - **Location:** United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Training Data, A/B Testing, Artificial Intelligence, Airflow, Amazon Web Services, Architectural Patterns, Cloud Computing, Continuous Integration, Information Engineering, Data Transformation, DevOps, Identity and Access Management, Python (Programming Language), Key Management, Machine Learning, Cloud Services, Prometheus, Azure Machine Learning, SQL Databases, Management of Software Versions, Feature Engineering, Data Ingestion, Delivery Pipeline, Large Language Models, Snowflake, Grafana, Multi-Agent Systems, Apache Spark, Kubernetes, Infrastructure Automation Frameworks, Data Lineage, Machine Learning Operations, Functional Programming, Cloudwatch, Api Gateway, Terraform, Software Version Control, Databricks - **Published:** August 3, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=ddac9e239bbef4c1 ## About the Role * 8+ years working in ML/AI engineering or MLOps roles with strong architecture exposure. * Strong expertise in AWS cloud-native ML stack, including: SageMaker(primary), EKS, Lambda, API Gateway, CI/CD (CodeBuild/CodePipeline or equivalent) * Hands-on experience with at least one major MLOps toolset and awareness of alternatives: MLflow, Kubeflow, SageMaker Pipelines, Airflow, BentoML, KServe, Seldon. * Deep understanding of model lifecycle management (feature engineering->training registry deployment monitoring). * Experience implementing or supporting LLMOps pipelines, including: prompt versioning, evaluation metrics, automation frameworks * Deep understanding of ML lifecycle: data ingestion, feature engineering, training, evaluation, model packaging, CI/CD, drift detection, monitoring, and governance. * Strong experience with AWS SageMaker (Pipelines, Feature Store, Model Registry, Model Monitor). * Experience implementing ML CI/CD pipelines including automated training, testing, validation, model promotion, and endpoint deployment. * Experience working on Infrastructure as Code (IaC) tools and CI/CD pipelines * Experience with Kubernetes based development * Experience with feature engineering pipelines and Feature Store management. * Understanding of lineage tracking: training data snapshot, feature versions, code versioning, metadata tracking, reproducibility. * Hands-on experience with AWS Bedrock and Agentcore service * Experience with CloudWatch, SageMaker Model Monitor, Prometheus/Grafana. * Strong foundation in Python and cloud-native development patterns. * Solid understanding of security best practices, IAM, secrets management, and artifact governance. Good to have skills: * Experience with vector databases, RAG pipelines, or multi-agent AI systems. * Exposure to DevOps and infrastructure-as-code (Terraform, Helm, CDK). * Hands-on understanding of model drift detection, A/B testing, canary rollouts, and blue-green deployments. * Familiarity with Observability stacks (Prometheus, Grafana, CloudWatch, OpenTelemetry). * SQL and data transformation experience using Snowflake, Databricks, Spark. * Ability to translate business goals into scalable AI/ML platform designs. * Strong communication and cross-team collaboration skills. * Ability to guide engineering teams through technical uncertainty and design choices. ## Description * Architect and implement the MLOps strategy for the programme, ensuring alignment with the project proposal and delivery roadmap. * Design and own enterprise-grade ML/LLM pipelines covering model training, validation, deployment, versioning, monitoring, and CI/CD automation. * Build container-oriented ML platforms (EKS-first) while evaluating alternative orchestration tools with similar capabilities (Kubeflow, SageMaker, MLflow, Airflow, etc.). * Implement hybrid MLOps + LLMOps workflows, including prompt/version governance, evaluation frameworks, and monitoring for LLM-based systems. * Serve as a technical authority across multiple internal and customer projects, contributing architectural patterns, best practices, and reusable frameworks. * Enable observability, monitoring, drift detection, lineage tracking, and auditability across ML/LLM systems. * Define and implement standards for model deployment, monitoring, governance, and automation to ensure production-grade reliability and scalability. * Collaborate with cross-functional teams - data engineering, platform, DevOps, and client stakeholders - to deliver production-ready ML solutions. * Ensure all solutions adhere to security, governance, and compliance expectations, particularly around handling cloud services, Kubernetes workloads, and MLOps tools. * Conduct architecture reviews, troubleshoot complex ML system issues, and guide teams through implementation across cloud-native ML platforms. * Mentor engineers and provide guidance on modern MLOps tools, platform capabilities, and best practices. If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us! ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [5 steps for running a Kubernetes environment at scale](https://www.wearedevelopers.com/videos/88-5-steps-for-running-a-kubernetes-environment-at-scale) - [The state of MLOps - machine learning in production at enterprise scale](https://www.wearedevelopers.com/videos/369-the-state-of-mlops-machine-learning-in-production-at-enterprise-scale) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [#90DaysOfDevOps - The DevOps Learning Journey](https://www.wearedevelopers.com/videos/548-90daysofdevops-the-devops-learning-journey) ## Related Articles - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path)