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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Machine Learning Engineer, AI Studio - **Company:** Amgen - **Location:** United States (Remote available) - **Experience:** Expert - **Salary:** $156,190.0 - $211,316.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Cloud Computing, Continuous Integration, Disaster Recovery, Python (Programming Language), Machine Learning, Azure Machine Learning, Runbook, SQL Databases, Management of Software Versions, Feature Engineering, Apache Spark, Deep Learning, Kubernetes, Infrastructure Automation Frameworks, Information Technology, Machine Learning Operations, Serverless Computing, GXP, Databricks - **Published:** August 3, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=5b420dddcd16bc5e ## About the Role Doctorate degree OR Master's degree and 2 years of experience in Computer Science, IT or related fields OR Bachelor's degree and 4 years of experience in Computer Science, IT or related fields OR Associate's degree and 8 years of experience in Computer Science, IT or related fields OR High school diploma / GED and 10 years of experience in Computer Science, IT or related fields Preferred Qualifications: * Demonstrated end-to-end ownership of at least one production ML, GenAI, software, data or automation system that delivered a measurable outcome. * Strong hands-on proficiency in Python and SQL, with experience designing production software, services and evaluation pipelines. * Advance capability in at least one role-defining pillar-Applied ML, GenAI/RAG/agents or ML platform/MLOps-plus credible depth across the production lifecycle. * Advanced ML, causal and uncertainty methods: Experience with data-centric AI, weak supervision, active learning, conformal or Bayesian uncertainty, causal inference, time-series, survival methods or drift-aware retraining. * Advanced deep learning and model efficiency: Experience with transformers, multimodal pipelines, CNNs, RNNs, GNNs, PEFT or LoRA, fine-tuning, distillation, quantization, routing, cascades or inference optimization. * Cloud, platform and AI operations: Experience with AWS, Bedrock or SageMaker, Databricks, Spark, Kubernetes, serverless systems, infrastructure as code, MLflow, Airflow, Kubeflow, observability and FinOps. * Human-AI and regulated delivery: Experience with review, correction, approval, accessibility, uncertainty communication, workflow automation and GxP-relevant or validated systems. * Strong product thinking and ability to connect technical decisions to user, workflow, risk, cost and business value. * Technical leadership, mentoring and constructive challenge while remaining hands-on. * Excellent analytical judgment and clear communication of evidence, uncertainty, trade-offs and limitations. * Cross-functional leadership across business, product, architecture, engineering and control functions. * Ownership, resilience and continuous improvement through incidents, feedback and measured outcomes. ## Description * Define the user, workflow, decision, intended use, baseline, value hypothesis, acceptance criteria, adoption path, operating owner and measurable technical and business outcomes. * Map rules, exceptions, data dependencies and human decision points before selecting deterministic automation, classical ML, deep learning, GenAI, RAG, agents or a manual approach. * Own production architecture across data, feature and knowledge pipelines, models, retrieval, agents, APIs, persistence, workflows, user experience, security zones and human review. * Lead hands-on development of production software, EDA, feature engineering, predictive models, deep-learning or NLP components, inference services, RAG, agent tools and workflow orchestration. * Establish baselines, experiment design, leakage controls, uncertainty, calibration, subgroup and robustness checks, gold sets, error taxonomies, expert adjudication and release thresholds. * Establish MLOps/LLMOps for lineage, reproducibility, versioning, CI/CD, canary or shadow release, observability, drift monitoring, SLOs, rollback, incidents, disaster recovery, capacity, cost and runbooks. * Coordinate security, privacy, Responsible AI, Quality, legal, model-risk and GxP controls; create reusable capabilities, measure adoption and value, mentor engineers and improve delivery practices. ## Related Videos - [Blueprints for Success: Steering a Global Data & AI Architecture](https://www.wearedevelopers.com/videos/1577-blueprints-for-success-steering-a-global-data-ai-architecture) - [Navigating the AI Revolution in Software Development](https://www.wearedevelopers.com/videos/1266-navigating-the-ai-revolution-in-software-development) - [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) - [Technical Documentation - How Can I Write Them Better and Why Should I Care?](https://www.wearedevelopers.com/videos/681-technical-documentation-how-can-i-write-them-better-and-why-should-i-care) - [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) - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [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 And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)