Senior AI / Data Engineer
New
I
Intertwine AssociatesAI and data platforms
Source API remote eligibility restrictions: United StatesFull-TimeSenior
SalaryThe anticipated base salary range for this position is $130,000-$175,000 per year.
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Job Details
- Experience
- 7+ years of experience building data pipelines, machine learning systems, or AI applications in production, including 2+ years leading technical work.
- Required Skills
- AWSPythonSQLGCPNumpyPyTorchAzurePandasTensorflowscikit-learn
Requirements
- Bring 7+ years of experience building data pipelines, machine learning systems, or AI applications in production, including 2+ years leading technical work.
- Demonstrate expert Python, including Pandas and NumPy, and advanced SQL.
- Have experience integrating relational and unstructured data sources.
- Bring deep hands-on experience with Azure data services, or equivalent depth in AWS or Google Cloud.
- Have production experience with PyTorch, TensorFlow, or scikit-learn.
- Have experience with data validation, schema enforcement, quality assurance, and governance in regulated environments.
- Have experience designing APIs and microservices for analytics and ML platforms.
- Demonstrate strong client engagement and written and verbal communication, including briefing senior, non-technical audiences.
- A bachelor's or advanced degree in a related field, or equivalent professional experience, is an additional qualification.
- Experience with large language models, Databricks, Snowflake, Microsoft Fabric, federal data security requirements, or federal agency data is an additional qualification.
Responsibilities
- Architect and lead delivery of enterprise data platforms and pipelines on Azure or equivalent cloud services.
- Set standards for ETL design, schema enforcement, data validation, quality checks, and lineage, and review other engineers' work.
- Design, build, and deploy machine learning and AI solutions, including large language model applications with evaluation and guardrails.
- Build secure API endpoints and microservices for analytics and ML platforms.
- Establish MLOps practices for experiment tracking, model registries, CI/CD, containers, monitoring, and cost and performance tuning.
- Apply data and AI governance for compliance, reproducibility, auditability, and responsible AI, including the NIST AI Risk Management Framework.
- Translate mission and business needs into technical roadmaps, estimates, and briefings for client leadership.
- Mentor engineers and data scientists and lead technical work across multidisciplinary teams.
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