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PlanIT Group, LLC · 4 months ago

Senior AI (IT Python and AI programming (SME) Engineer

United States

Full-time

Remote

Senior Level, Lead/Staff

20+ years exp

PlanIT Group, LLC is seeking a Senior AI Engineer to assist the Vice President of Information Technology in designing, developing, and maintaining IT systems capabilities. The role involves creating scalable solutions using AI tools and machine-learning models, while also ensuring the implementation of a comprehensive IT roadmap.

ConsultingInformation TechnologyManagement Consulting

Growth Opportunities

Responsibilities

Chosen resource must demonstrate these capabilities through actual work experience not merely training:

Practical Application of Core Python Concepts:

Not just knowing Python syntax but demonstrating a track record of building and deploying Python applications or scripts that address IT operational needs, automate processes, or handle data management

Data Engineering and Analysis Skills:

Demonstrable experience with data acquisition, cleaning, preprocessing, and transformation using Python tools and techniques for building robust analysis on large scale data sets

Implementing and Deploying Cloud Applications:

Experience deploying python applications in cloud service production environments (e.g., AWS, Azure, GCP), potentially leveraging containerization tools (e.g., Docker, Kubernetes)

Understanding of Software Engineering Best Practices:

Experience in applying principles like version control (Git), writing clear and testable code, participating in code reviews, and using continuous integration/continuous deployment (CI/CD) pipelines

Knowledge of Data Science Best Practices:

Demonstrated understanding and implementation of data science solutions such as data pipelining, feature engineering, or creation of Machine Learning Models

Familiarity with Cloud-based Data Science Services:

Proficiency using managed AI/ML services provided by cloud platforms to streamline development, deployment, and management of data science applications

Ethical Practices and Security Knowledge:

A demonstrated awareness and application of ethical guidelines for data science solutioning, including addressing bias, ensuring data privacy, and implementing secure coding practices in Python-based solutions

An individual whose qualifications and/or particular expertise are exceptional and/or highly specialized or unique. and are typically identified as recognized industry leaders: and typically initiates, supervises, and/or develops requirements from a project's inception to conclusion for complex to extremely complex programs. and also provides strategic and expert advice and technical guidance, to program and project staff

This individual also provides detailed analysis, evaluation and recommendations for improvements, optimization development, and/or maintenance efforts for client-specific or mission critical challenges/issues and consults with client to define needs or challenges. while also supervising studies: leading surveys and performing data collection for analyses used to advise and recommend/suggest solutions

Chosen resource should exhibit through actual work experience not merely training:

Desirable: hands on experience building MCP servers and integration with Agentic AI workflows

Communicating complex technical concepts to both technical and executive stakeholders

Proficiency creating technical diagrams with products like Microsoft Visio or Draw.io

Proficiency creating technical design and architecture documents in Microsoft Word

Proficiency creating business and technical presentations in Microsoft PowerPoint

Proficiency creating data representations, charts and reports in tools such as Microsoft's Excel worksheets and Power BI

Ability to communicate, orally and in writing, sufficient to develop and present management briefings; provide written and/or verbal guidance on technical issues; and prepare/present recommendations and reports

Using design patterns for building scalable and maintainable applications/solutions

Clearly document code, models, and technical solutions

Proficiency in Generative AI and prompt engineering

Continuous learning and adaptability in a very large IT organization

Troubleshooting software and technical implementations in large-scale enterprise ecosystems

API development and integration

Querying and managing data in both SQL and NoSQL databases

Tasks – might include (neither exhaustive nor restrictive):

Data science tasks such as data acquisition, data cleaning, and feature extraction

Develop and demonstrate proof-of-concepts (PoC); independently or in a team

Create technical diagrams and documentation to show PoC implementations and potential production implementation

Researching and presenting to teammates on the latest tools/packages/capabilities being developed

Make recommendations on relevant tools/packages to use for production environments

Work with relevant governance committees to document and obtain approval for exploratory data science efforts

Consulting with members of architecture teams to identify potential automation solutions which may include AI/ML

Collaborating with cross functional teams on holistic AI/ML solutions

Qualification

PythonMachine LearningCloud ApplicationsData EngineeringAI ToolsData Science Best PracticesAPI DevelopmentSQLNoSQLGenerative AITroubleshootingTechnical DocumentationSoft SkillsContinuous Learning

Required

Practical Application of Core Python Concepts: Not just knowing Python syntax but demonstrating a track record of building and deploying Python applications or scripts that address IT operational needs, automate processes, or handle data management

Data Engineering and Analysis Skills: Demonstrable experience with data acquisition, cleaning, preprocessing, and transformation using Python tools and techniques for building robust analysis on large scale data sets

Implementing and Deploying Cloud Applications: Experience deploying python applications in cloud service production environments (e.g., AWS, Azure, GCP), potentially leveraging containerization tools (e.g., Docker, Kubernetes)

Understanding of Software Engineering Best Practices: Experience in applying principles like version control (Git), writing clear and testable code, participating in code reviews, and using continuous integration/continuous deployment (CI/CD) pipelines

Knowledge of Data Science Best Practices: Demonstrated understanding and implementation of data science solutions such as data pipelining, feature engineering, or creation of Machine Learning Models

Familiarity with Cloud-based Data Science Services: Proficiency using managed AI/ML services provided by cloud platforms to streamline development, deployment, and management of data science applications

Ethical Practices and Security Knowledge: A demonstrated awareness and application of ethical guidelines for data science solutioning, including addressing bias, ensuring data privacy, and implementing secure coding practices in Python-based solutions