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Forward Deployed Engineer AI/ML
Career Insights for Machine Learning Engineer
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What they do
A Machine Learning Engineer specializes in designing, building, and deploying machine learning models. They utilize statistical and mathematical techniques, parallelizing processing, hyperparameter tuning, and other optimization methodologies to improve model performance. Responsibilities also include collecting and preprocessing large datasets, conducting exploratory data analysis, working closely with data engineers to understand data requirements, and engineer input variables for machine learning models.
$124,597 / year median in the U.S.
Job Description
- Design and deliver end-to-end AI solutions, including agentic AI workflows, RAG pipelines, and enterprise AI applications.
- Build and deploy scalable AI/ML systems in production environments.
- Develop data pipelines and integrate AI solutions with enterprise platforms, databases, and business systems.
- Lead solution architecture, prototyping, deployment, and post-production optimization.
- Collaborate with customer stakeholders to translate business requirements into technical solutions.
- Drive best practices around DevOps, observability, monitoring, and AI governance. Required Qualifications
- Palantir certification is required.
- 6+ years of experience in software engineering, data engineering, or AI/ML.
- 4+ years of experience in customer-facing, consulting, solutions engineering, or field engineering roles.
- Proven experience building and deploying AI/ML applications at enterprise scale.
- Strong Python development skills with full-stack engineering experience.
- Hands-on experience with LLMs, RAG architectures, vector databases, prompt engineering, and AI orchestration frameworks.
- Experience with data engineering, ETL/ELT pipelines, and large-scale data integration.
- Strong DevOps experience with Docker, Kubernetes, CI/CD, and cloud-native deployments.
- Excellent communication skills with the ability to engage technical and business stakeholders. Preferred Qualifications
- Experience designing and implementing agentic AI workflows and enterprise AI applications.
- Experience with AI governance, observability, and production monitoring.
- Experience delivering technical solutions directly within enterprise customer environments. Certifications
- Palantir certification is required.