Senior Machine Learning Engineer, AI Studio
New
J
JobgetherHealthcare AI
Based in the United StatesFull-TimeSenior
Salary$156,190.05 - $211,315.95 USD
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Job Details
- Experience
- Doctorate degree, or Master’s degree with 2+ years of relevant experience, Bachelor’s degree with 4+ years of relevant experience, Associate’s degree with 8+ years of relevant experience, or equivalent professional experience.
- Required Skills
- AWSPythonSQLKubernetesMachine LearningMLOpsGenerative AI
Requirements
- Doctorate degree, or Master’s with 2+ years, Bachelor’s with 4+ years, or Associate’s with 8+ years of relevant experience.
- Proven experience owning and delivering at least one production ML, generative AI, software, or data system.
- Strong hands-on proficiency in Python and SQL.
- Advanced expertise in applied ML, generative AI, RAG systems, AI agents, ML platforms, or MLOps.
- Experience with advanced ML techniques (e.g., causal inference, uncertainty modeling, time-series analysis).
- Experience with deep learning technologies (e.g., transformers, fine-tuning, model optimization).
- Knowledge of cloud and AI infrastructure such as AWS, SageMaker, Databricks, Spark, Kubernetes, MLflow, or Airflow.
- Experience implementing AI governance, human-in-the-loop workflows, and validation processes.
- Strong understanding of software engineering principles, APIs, data pipelines, and scalable system design.
- Demonstrated technical leadership and mentoring ability.
- Excellent analytical judgment and communication skills.
Responsibilities
- Define AI solution objectives by establishing user needs, workflows, success metrics, and measurable outcomes.
- Evaluate business problems to determine appropriate technical approaches, including classical ML, generative AI, RAG, and AI agents.
- Design and own production architectures for data pipelines, knowledge systems, models, APIs, and workflows.
- Develop and maintain production machine learning models, NLP solutions, and evaluation pipelines.
- Implement MLOps and LLMOps capabilities to ensure reproducibility, version control, and deployment automation.
- Establish robust evaluation frameworks for testing, quality benchmarks, and error classification.
- Partner with security, privacy, legal, and quality teams to ensure responsible AI implementation.
- Mentor engineers and promote strong engineering practices across AI development processes.
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