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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Operations Architect & AWS Engineer, Life... - **Company:** Managed Markets Insight & Technology, LLC - **Location:** Charleston, WV, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Amazon Elastic Compute Cloud, Amazon S3, Big Data, Configuration Management, Cyber Security, Data Security, DevOps, Elasticsearch, Python (Programming Language), Machine Learning, Regression Testing, Management of Software Versions, Autoscaling, Large Language Models, Multi-Agent Systems, Cloudformation, Information Technology, Machine Learning Operations, Terraform, Data Pipelines - **Published:** July 28, 2026 - **Apply:** https://www.juju.com/job/00000000gk6qam ## About the Role * Master's in Computer Science, Data Science, or related field. * 5+ years in production NLP/ML systems development and operations on AWS. * Hands-on experience deploying and serving open-weight LLMs (Llama, Gemma, Qwen families) at scale, using frameworks such as vLLM, TGI, or equivalent serving infrastructure. * Expert Python proficiency and working knowledge of LLM orchestration tooling (e.g., LangChain/LangGraph, custom agent frameworks). * Experience with AWS ML/compute services (SageMaker, EC2 GPU instances, S3, Step Functions, Lambda) and infrastructure-as-code (Terraform, CloudFormation, or CDK). * Proven track record deploying large-scale NLP/LLM solutions in healthcare or life sciences. * Strong analytical and communication skills; comfortable operating across multiple concurrent projects. Preferred Qualifications * AWS certifications (Solutions Architect, Machine Learning Specialty, or DevOps Engineer). * Direct experience with PHI handling and HIPAA-compliant ML infrastructure. * Experience with AWS Redshift, OpenSearch/Elasticsearch, or similar large-scale data stores. * Familiarity with ML experiment tracking and lifecycle management (MLflow, Weights & Biases, or equivalent). * Experience building or operating RAG systems, structured extraction pipelines, or clinical NLP applications. ## Description * Design and maintain AWS cloud architectures optimized for LLM inference and fine-tuning workloads, including GPU instance management, auto-scaling, and cost optimization across spot and on-demand capacity. * Build and operate agentic AI pipelines - multi-step LLM orchestration workflows with tool use, structured output extraction, retrieval-augmented generation (RAG), and automated quality control loops. * Deploy and manage open-weight LLMs (Llama, Gemma, Qwen, and successors) in production, including model serving infrastructure, batching strategies, and latency/throughput optimization. * Implement robust evaluation and monitoring frameworks for LLM-based extraction systems: automated accuracy measurement, drift detection, prompt regression testing, and human-in-the-loop QC integration. * Develop and maintain CI/CD pipelines for prompt versioning, model updates, and configuration management across development, staging, and production environments. * Collaborate with NLP scientists, data engineers, and software developers to translate prototype extraction logic into production-hardened, recurrent processing feeds. * Ensure compliance with data security requirements (HIPAA, PHI handling) in all LLM deployment and data processing workflows, working closely with IT security and compliance teams. * Analyze and optimize computational resource utilization - GPU hours, storage, network throughput - balancing cost efficiency against processing SLAs. * Evaluate and integrate emerging LLM serving technologies, orchestration frameworks, and inference optimization techniques (quantization, speculative decoding, structured generation) to maintain operational edge. ## Related Videos - [DevOps for AI: running LLMs in production with Kubernetes and KubeFlow](https://www.wearedevelopers.com/videos/1222-devops-for-ai-running-llms-in-production-with-kubernetes-and-kubeflow) - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [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) - [Infrastructure as Code: The Developer's Secret Weapon](https://www.wearedevelopers.com/videos/1221-infrastructure-as-code-the-developer-s-secret-weapon) - [DevOps Maturity Check – a way to balance autonomy and alignment](https://www.wearedevelopers.com/videos/58-devops-maturity-check-a-way-to-balance-autonomy-and-alignment) - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [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) - [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) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)