What are the responsibilities and job description for the AI/ML Engineer position at Jobs via Dice?
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Job Description,
Design, develop, and optimize complex data pipelines using machine learning engineering best practices to ensure scalability, efficiency, and reliability.
Develop and implement robust MLOps pipelines to support deployment, monitoring, and lifecycle management of AI/ML models in production.
Integrate and maintain data and model pipelines, proactively diagnosing data quality issues and documenting assumptions.
Collaborate with data scientists to validate model-ready datasets and ensure thorough feature documentation.
Conduct exploratory data analysis on raw data sources, incorporating business context to support model development.
Track data lineage and perform root cause analysis during early-stage exploration or issue resolution.
Partner with internal stakeholders to understand business processes and translate them into scalable analytical solutions.
Develop and maintain model monitoring scripts, investigate alerts, and coordinate timely resolution.
Competencies:
Strong analytical and problem-solving skills.
Ability to collaborate with cross-functional teams and mentor junior engineers.
Attention to detail and commitment to high-quality solutions.
Effective communication and documentation skills.
Strong understanding of AI/ML development lifecycle and best practices.
Required Technical Skills:
Proficiency in Python and familiarity with key machine learning frameworks and libraries.
Hands-on experience building ETL pipelines using AWS services (minimum 3 years).
Experience developing and implementing ML pipelines for deploying, monitoring, and managing AI/ML models in production.
Strong understanding of cloud technologies and AI/ML platforms like AWS SageMaker.
Solid grasp of software engineering principles including design patterns, testing, security, and version control.
Experience designing and implementing end-to-end machine learning pipelines and solution architectures.
Must-Have Qualifications:
Bachelor s degree in a relevant field (Master s preferred).
7 years of relevant experience in AI engineering, machine learning engineering, or data engineering.
Proven experience in building production-ready AI/ML solutions and pipelines.
Strong understanding of AI/ML platforms, development lifecycle, and software engineering best
Job Description,
Design, develop, and optimize complex data pipelines using machine learning engineering best practices to ensure scalability, efficiency, and reliability.
Develop and implement robust MLOps pipelines to support deployment, monitoring, and lifecycle management of AI/ML models in production.
Integrate and maintain data and model pipelines, proactively diagnosing data quality issues and documenting assumptions.
Collaborate with data scientists to validate model-ready datasets and ensure thorough feature documentation.
Conduct exploratory data analysis on raw data sources, incorporating business context to support model development.
Track data lineage and perform root cause analysis during early-stage exploration or issue resolution.
Partner with internal stakeholders to understand business processes and translate them into scalable analytical solutions.
Develop and maintain model monitoring scripts, investigate alerts, and coordinate timely resolution.
Competencies:
Strong analytical and problem-solving skills.
Ability to collaborate with cross-functional teams and mentor junior engineers.
Attention to detail and commitment to high-quality solutions.
Effective communication and documentation skills.
Strong understanding of AI/ML development lifecycle and best practices.
Required Technical Skills:
Proficiency in Python and familiarity with key machine learning frameworks and libraries.
Hands-on experience building ETL pipelines using AWS services (minimum 3 years).
Experience developing and implementing ML pipelines for deploying, monitoring, and managing AI/ML models in production.
Strong understanding of cloud technologies and AI/ML platforms like AWS SageMaker.
Solid grasp of software engineering principles including design patterns, testing, security, and version control.
Experience designing and implementing end-to-end machine learning pipelines and solution architectures.
Must-Have Qualifications:
Bachelor s degree in a relevant field (Master s preferred).
7 years of relevant experience in AI engineering, machine learning engineering, or data engineering.
Proven experience in building production-ready AI/ML solutions and pipelines.
Strong understanding of AI/ML platforms, development lifecycle, and software engineering best
