Senior Applied AI Engineer
P
phDataData and AI
Employees across the United States, Latin America, and India, partnering with colleagues across time zonesFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years of experience in AI/ML engineering, software engineering, data engineering, or a related technical discipline, including at least 3 years designing, building, deploying, or operating AI and machine learning solutions in production.
- Required Skills
- AWSPythonSQLCloud ComputingMachine LearningSnowflakeGenerative AI
Requirements
- 5+ years of experience in AI/ML engineering, software engineering, data engineering, or a related technical discipline.
- At least 3 years designing, building, deploying, or operating AI and machine learning solutions in production.
- Strong programming experience in Python for building production-grade AI, ML, and data pipelines.
- Strong working knowledge of SQL for querying, transforming, and validating data.
- Practical experience with modern cloud, data, and AI ecosystems such as Snowflake, Databricks, AWS, Azure, Google Cloud, dbt, Anthropic, or OpenAI.
- Understanding of production AI/ML concerns, including evaluation, observability, security, governance, and scalability.
- Experience developing production software, APIs, services, or AI applications for enterprise environments.
- Bachelor's degree in a relevant field or equivalent practical experience.
Responsibilities
- Build and operationalize production-ready Applied AI and machine learning solutions that align with customer objectives, technical requirements, and enterprise environments.
- Translate solution architectures and business requirements into clean, maintainable, well-tested code, data and model pipelines, and technical deliverables for AI applications.
- Implement robust production patterns for predictive ML, generative AI, LLM applications, agentic workflows, and intelligent automation, including evaluation, monitoring, and guardrails.
- Integrate AI solutions with enterprise data platforms, applications, APIs, and workflows, ensuring reliability, security, performance, and cost-effectiveness in production.
- Contribute to reusable accelerators, reference implementations, engineering standards, and technical playbooks that improve how phData delivers Applied AI solutions across clients.
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