Associate Machine Learning Engineer

Career Category

Manufacturing

Job Description

ABOUT THE ROLE

Role Description:

The Associate Machine Learning Engineer position offers a unique opportunity to join a fun, innovative engineering team within the AI & Data Science (AI&D) organization. You’ll work on next-generation capabilities and services in Applied AI & Automation using innovative COTS products, open-source software, frameworks, tools, and cloud computing services. The role also emphasizes demonstrating these capabilities to support critical business operations and initiatives, in ensuring quality, compliance, and performance across Amgen’s Applied AI & Automation Footprint.

The role is responsible for building and scaling our AI and machines learning solutions from development to production. Your expertise in MLOps will be essential in creating efficient and reliable ML pipelines. The role represents working in the solution engineering and DevOps teams to lead the transition from Intelligent automation to Agentic Process Automation and ensures that strategy and implementation remain connected throughout the value stream. The ideal candidate has experience in building AI & ML solutions, has excellent communication skills, and an understanding of Agile methodologies.

This role focuses on supporting the development, deployment, operation, and reliability of AI and LLM‑based solutions in production environments, working under guidance of senior engineers and platform teams. The position emphasizes operational excellence, observability, and compliance for applied AI and GenAI systems.

Roles & Responsibilities:

Collaborate with data scientists to develop, train, and evaluate machine learning models.

Build and maintain MLOps pipelines, including data ingestion, feature engineering, model training, deployment, and monitoring.

Leverage cloud platforms (AWS, Databricks) for ML model development, training, and deployment.

Develop solutions using DevSecOps framework that are secure, scalable, reliable, and aligned with enterprise architecture standards.

Evaluate model performance using appropriate metrics and optimize models for accuracy and efficiency

Develop and execute unit tests, integration tests, and other testing strategies to ensure the quality of the software

Create and maintain documentation on software architecture, design, deployment, disaster recovery, and operations

Identify and resolve technical challenges effectively

Provide ongoing support and maintenance for applications, ensuring that they operate smoothly and efficiently

Analyze customer feedback and support data to identify pain points and opportunities for improvement

Evaluate and recommend technologies and tools that best fit the solution requirements

Support operationalization of machine learning and GenAI models developed by data scientists and solution teams.

Assist in evaluating model and LLM performance using metrics related to reliability, efficiency, and response quality.

Support deployment and operation of LLM‑based workflows, including prompt configurations, retrieval‑augmented generation (RAG) pipelines, and agent‑based automations.

Assist with monitoring AI and LLM systems for availability, latency, error rates, and quality degradation.

Support model, prompt, and pipeline versioning across development, test, and production environments.

Participate in incident triaging, root cause analysis, and rollback or mitigation activities for AI services.

Assist with evaluation runs for LLM outputs, including grounding, reliability, and safety checks.

Follow established AI governance, security, and compliance standards when operating AI and GenAI solutions.

Monitor AI and LLM endpoints for availability, latency, throughput, and error rates using enterprise monitoring tools.

Assist with dashboards, alerts, runbooks, and operational documentation to support reliable AI system operations.

Basic Qualifications and Experience:  

Any degree and 3 to 5 years of Computer Science, IT or related field experience

Functional Skills:

Must-Have Skills:

Strong foundations in machine learning algorithms and techniques

Experience in MLOps practices and tools (e.g., MLflow, Kubeflow, Airflow); Experience in model monitoring, including model observability and explainability

Proficiency in Python (or R) and relevant ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn)

Experience with big data technologies (e.g., Spark, Hadoop), and performance tuning in query and data processing

Understanding of the LLM inference lifecycle, including prompt execution, retrieval, and response generation.

Awareness of common LLM failure modes such as hallucinations, prompt injection, and data leakage.

Good-to-Have Skills:

Good understanding of cloud platforms (e.g., AWS, Databricks) and containerization technologies (e.g., Docker, Kubernetes)

Experience with monitoring and logging tools (e.g., Prometheus, Grafana, Splunk)

Experience with data processing tools like Hadoop, Spark, or similar

Knowledge of GenAI tooling: vector databases, RAG pipelines, prompt-engineering DSLs and agent frameworks (e.g., LangChain, Semantic Kernel).

Ability to analyze client requirements and translate them into solutions

Exposure to LLM evaluation techniques beyond accuracy, including grounding, faithfulness, latency, and reliability of metrics.

Soft Skills:

Excellent critical-thinking and problem-solving skills

Strong communication and collaboration skills

Demonstrated awareness of how to function in a team setting

Demonstrated awareness of presentation skills

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