Agentic AI Engineer - Core Platforms
About The Position
The Implementation / ML Ops Engineer implements, deploys, and supports AI/ML models within approved GPU-enabled environments. The role packages and operationalizes trained models, supports scalable data and deployment pipelines, integrates models with production systems, and helps maintain monitoring, validation, versioning, and rollback processes. The engineer works within enterprise patterns, security controls, and AI governance standards, collaborating with product, technology, and business partners to deliver production-ready AI capabilities. The role develops and integrates components of production AI systems, including ML models and LLM-based services, with moderate independence. It contributes to CI/CD, container, Kubernetes, registry, testing, and telemetry configurations for assigned workloads, and troubleshoots common implementation issues while escalating complex design, optimization, or production problems to senior engineers.
Requirements
- At least 2 years of software development experience, including hands-on experience with machine learning, AI technologies, and Agentic AI solutions.
- Bachelor's degree in Computer Science, Machine Learning, Data Science, or comparable experience.
- Knowledge of Python and data-processing technologies.
- Working knowledge of machine-learning techniques and model-evaluation approaches.
- Foundational working knowledge of MLOps or LLMOps, containers, Kubernetes fundamentals, CI/CD, artifact management, testing, and monitoring.
- Basic understanding of GPU resource needs, runtime dependencies, scheduling constraints, and enterprise security and change controls.
- Experience working with large language models, especially GPT-3.5/4 or similar models.
- Experience with programming languages such as Python or R.
- Experience deploying AI models and pipelines using machine-learning operations tools.
- Experience with CI/CD pipelines and containerized deployments.
- Experience developing and deploying machine-learning or LLM-enabled applications in production or near-production environments.
- Practical experience with distributed-systems concepts, APIs, and cloud-native infrastructure supporting AI workloads.
- Moderate expertise in a specific technical domain.
- Some practical experience contributing to software, infrastructure, container, data, or machine-learning deployment and automation work.
- Exposure through project, internship, or practical work to containerized deployment, artifact repositories, automated testing, monitoring, versioning, or rollback.
- Must be eligible to work in the United States for American Express; the company will not pursue visa sponsorship.
Nice To Haves
- An advanced degree is preferred.
Responsibilities
- Implement model monitoring and evaluation techniques to improve AI system performance over time.
- Ensure AI solutions adhere to governance, security, and compliance requirements.
- Support and troubleshoot AI models and data-processing pipelines in production.
- Collaborate with product and business teams to align technology initiatives with business objectives.
- Perform standard tasks within established procedures and guidelines, working with some independence on routine tasks.
- Develop and integrate components of production AI systems, including ML models or LLM-based services, with moderate independence.
- Apply enterprise standards for observability, safety, and compliance in implemented AI components.
- Implement and support model packaging, validation, versioning, deployment, monitoring, and rollback using approved templates and controls.
- Contribute to CI/CD, container, Kubernetes, registry, testing, and telemetry configurations for assigned workloads.
- Troubleshoot common implementation issues and escalate complex design, optimization, or production problems to senior engineers.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
