Lead Data Engineer

  • Atlanta, GA
  • Posted 1 day ago | Updated 1 day ago

Overview

On Site
Contract - W2
Contract - CON_W2

Skills

Programming skills (Python
Java) with expertise in machine learning frameworks (TensorFlow
PyTorch)
data engineering
and pipeline building. Experience applying generative AI technologies in practical scenarios.

Job Details

Job title :: Lead Data Engineer/AI Implementation Lead (ONLY W2)
Location ::Remote
Duration :: 6 Months Contract to Hire


Position Overview:


The AI Implementation Lead is responsible for overseeing the end-to-end implementation of AI projects within the organization. This role involves managing project lifecycles, collaborating with technical and business stakeholders, and ensuring that AI solutions are scalable, efficient, and aligned with enterprise goals. The AI Implementation Lead will play a crucial role in delivering generative AI solutions while maintaining compliance with ethical and data governance standards.


Key Responsibilities:


1. Project Management:
Oversee the complete lifecycle of AI projects from planning to execution.
Manage project budgets, timelines, resources, and stakeholder communications.
Apply Agile methodologies to ensure efficient project management and delivery.
2. Solution Design:
Collaborate with AI solution architects to design scalable, robust AI solutions.
Ensure that AI solutions align with enterprise architecture and business objectives.
Lead the development of generative AI solutions, focusing on data management and model training.
3. Stakeholder Engagement:
Engage with business units to understand requirements and translate them into technical specifications.
Facilitate workshops and meetings to gather feedback and refine AI implementations.
Act as a liaison between technical teams and business stakeholders to ensure project alignment.
4. Risk and Compliance Management:
Identify potential risks related to AI implementations and develop mitigation strategies.
Ensure compliance with ethical guidelines and data governance policies.
Conduct data privacy impact assessments (DPIAs) for AI projects.
5. Training and Support:
Provide training sessions for end-users and stakeholders on AI solution usage.
Offer ongoing support and maintenance for deployed AI systems.
Develop training materials and documentation to aid users in understanding new AI tools.


Qualifications:


Programming skills (Python, Java) with expertise in machine learning frameworks (TensorFlow, PyTorch), data engineering, and pipeline building.
Experience applying generative AI technologies in practical scenarios.
Skilled in managing AI projects using Agile methodologies, with strong stakeholder management and communication abilities.
Proficient in AI architecture, cloud platforms (Google Cloud Platform, Azure, AWS), and data visualization tools (Tableau, Power BI).
Knowledge of ethical AI practices and data governance, with problem-solving skills to address implementation risks.
Ability to simplify complex technical concepts for non-technical stakeholders.

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