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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # MLOps Architect in Arlington - **Company:** Energy Jobline - **Location:** Arlington, VA, United States - **Experience:** Expert - **Salary:** $117,800.0 - $189,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Amazon S3, Computing Platforms, Encodings, Databases, Continuous Integration, Information Engineering, Data Governance, Data Transformation, Database Storage Structures, Identity and Access Management, Python (Programming Language), Machine Learning, Azure Machine Learning, Systems Integration, Management of Software Versions, Data Logging, Feature Engineering, Data Ingestion, Large Language Models, State Machines, Model Validation, Caching, Generative AI, Cloudformation, Kubernetes, Low Latency, Deployment Automation, Machine Learning Operations, Functional Programming, Cloudwatch, Terraform, Docker, Databricks - **Published:** September 4, 2026 - **Apply:** https://www.energyjobline.com/job/mlops-architect-arlington-31445926 ## About the Role * 6+ years of experience in ML engineering, data engineering, or MLOps roles. * Proven experience architecting ML platforms in AWS. * Strong hands-on experience with SageMaker (training, pipelines, deployment). * Experience operationalizing LLM or Generative AI systems in production. * Experience building RAG pipelines and integrating vector databases. * Experience working with Databricks in production. * Experience implementing data governance and catalog systems (e.g., Atlan). * Strong understanding of CI/CD principles for ML and GenAI. * Experience with containerization (Docker) and orchestration (Kubernetes/EKS). * Deep knowledge of infrastructure-as-code (Terraform, CloudFormation). * Strong understanding of observability and monitoring for ML systems. * Experience implementing cloud cost optimization strategies (FinOps). * Strong Python proficiency. * Experience with foundation model fine-tuning and parameter-efficient methods. * Experience implementing model registries and experiment tracking tools. * Experience designing feature stores and embedding stores. * Familiarity with AI risk management, bias mitigation, and safety controls. * Experience supporting regulated or data-sensitive environments. * Platform-level architectural thinking. * Deep understanding of how to integrate GenAI into enterprise ML ecosystems. * Ability to balance scalability, governance, security, performance, and cost. * Strong technical leadership and cross-functional collaboration skills. * Hands-on ability to move from architecture design to implementation ## Description We are seeking a senior MLOps Architect to design and scale a modern ML and Generative AI platform across AWS. This role will own the architecture for traditional ML and LLM/Generative AI pipelines, ensuring production reliability, governance, cost optimization (FinOps), and enterprise-grade security. The ideal candidate has deep expertise in AWS, SageMaker, Databricks, Atlan (data catalog/governance), and modern MLOps tooling, and understands how to operationalize LLMs, RAG systems, and foundation models within a governed, scalable MLOps stack. This is a strategic, hands-on architecture role responsible for integrating GenAI capabilities into an enterprise ML platform. What you'll Do: MLOps & GenAI Platform Architecture * Design and implement scalable ML and LLM infrastructure on AWS (SageMaker, EKS, S3, IAM, Lambda, Step Functions, CloudWatch). * Architect end-to-end ML and Generative AI lifecycle workflows: * + Data ingestion & preprocessing o Feature engineering / embedding o Model training & fine-tuning (traditional ML + foundation models) + Model evaluation & validation + Deployment (real-time, batch, streaming) + Monitoring & retraining * Integrate LLM pipelines (prompt workflows, RAG architectures, fine-tuning flows) into the enterprise MLOps stack. * Define standards for CI/CD/CT pipelines across ML and GenAI workloads. Generative AI & LLM Operationalization * Architect Retrieval-Augmented (RAG) pipelines including: * + Embedding workflows + Vector database integration + Document ingestion and chunking strategies + Retrieval evaluation and monitoring * Design and deploy LLM-based services using: * + Managed services (e.g., SageMaker endpoints, Bedrock-style APIs) + Containerized custom inference services * Establish prompt versioning, evaluation frameworks, and experiment tracking for LLM systems. * Implement guardrails for hallucination control, safety monitoring, bias detection, and usage logging. * Define architecture for LLM fine-tuning workflows (including data curation, evaluation, and cost controls). * Implement scalable orchestration of LLM pipelines using workflow engines and event-driven patterns. Deployment, Monitoring & Reliability * Architect scalable inference patterns for: * + Traditional ML models + LLM APIs + RAG systems * Implement model monitoring frameworks for: * + Performance degradation + Drift detection + LLM output quality + Latency and token usage metrics * Define SLAs/SLOs for ML and GenAI systems. * Design safe deployment strategies (blue/green, canary, shadow testing). * Establish logging, observability, and traceability standards for GenAI systems FinOps & Cost Optimization * Implement cost tracking for: * + Training workloads o GPU utilization + Inference endpoints o Token consumption (LLM APIs) + Vector database storage * Optimize LLM workloads for cost-performance tradeoffs (model size, batching, caching strategies). * Design autoscaling and compute optimization strategies for GPU and CPU-based inference. * Partner with finance and engineering teams to forecast ML/GenAI infrastructure spend. Platform Enablement & Standards * Define enterprise standards for: * + Experiment tracking + Model registry + Prompt registry + Artifact management + Embedding versioning * Provide architectural guidance to data science, AI, and engineering teams. * Evaluate and recommend tooling across the ML/GenAI stack (MLflow, feature stores, vector databases, orchestration tools). * Drive documentation and reusable patterns for ML and GenAI development. ## Related Videos - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [HTTP headers that make your website go faster](https://www.wearedevelopers.com/videos/1676-http-headers-that-make-your-website-go-faster) - [Infrastructure as Code: The Developer's Secret Weapon](https://www.wearedevelopers.com/videos/1221-infrastructure-as-code-the-developer-s-secret-weapon) - [MLOps - What’s the deal behind it?](https://www.wearedevelopers.com/videos/392-mlops-what-s-the-deal-behind-it) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) - [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops) ## Related Articles - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [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) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [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)