Senior AI Field Engineer (Enterprise)
New
United StatesFull-TimeSenior
SalaryTotal target compensation (OTE) ranging from $220,000 to $280,000 USD
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Job Details
- Experience
- 3+ years
- Required Skills
- AWSPythonGCPKubernetesMachine LearningAzureLLMMLOps
Requirements
- 3+ years of experience in AI/ML engineering, solutions architecture, MLOps, or similar customer-facing technical roles.
- Proven track record of deploying AI/ML systems into live production environments, not limited to prototypes or conceptual work.
- Strong hands-on experience with large language models, including inference, fine-tuning, and open-source LLM frameworks.
- Proficiency in Python with strong software engineering fundamentals.
- Experience working with cloud platforms such as AWS, Azure, or GCP and containerized environments (e.g., Kubernetes).
- Familiarity with GPU optimization and high-performance compute environments.
- Strong understanding of modern AI deployment stacks, model serving infrastructure, and evaluation methodologies.
- Excellent communication skills with the ability to engage both engineering teams and senior business stakeholders.
- Ability to operate in fast-paced, high-ownership environments with shifting priorities and multiple concurrent projects.
- Strong problem-solving mindset with the ability to work across ambiguous and complex technical environments.
Responsibilities
- Lead technical discovery sessions with enterprise clients to define requirements, scope AI solutions, and design deployment strategies.
- Build, deploy, and optimize end-to-end generative AI proof-of-concepts and production systems within customer environments.
- Conduct performance testing, load evaluations, and model validation to ensure scalable and reliable AI deployments.
- Advise enterprise engineering teams on model selection, fine-tuning approaches, inference optimization, and evaluation frameworks.
- Serve as the primary technical owner for assigned accounts, aligning stakeholders and resolving complex architectural and integration challenges.
- Translate enterprise customer needs into actionable feedback for internal product and engineering teams to influence roadmap decisions.
- Navigate complex security, compliance, and infrastructure requirements across cloud and on-prem environments.
- Support enterprise rollout and productionization efforts in collaboration with customer engineering and internal teams.
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