Macpower Digital Assets Edge
GenAI/ML Engineer | Generative AI (GenAI) Solutions Architect
Macpower Digital Assets Edge, Columbia, South Carolina, us, 29228
bout the Role:
We are seeking an experienced
Generative AI Solutions Architect
to lead the design and implementation of cutting-edge GenAI solutions. You will define the architecture, lead development efforts, and ensure scalable, ethical deployment of AI systems-from model selection to production. This role requires deep technical expertise in
RAG, vector stores, prompt engineering, and ML deployment , along with strong leadership to guide teams and stakeholders.
Key Responsibilities: GenAI Architecture & Development
Define end-to-end
GenAI architecture , including model selection, fine-tuning, retrieval-augmented generation (RAG), vector databases, and prompt engineering pipelines. Design and deploy
scalable software applications
to support Generative AI initiatives. Build
Minimum Viable Products (MVPs)
for rapid iteration in dynamic environments. ML Engineering & Deployment
Hands-on
model deployment
from development to production, with troubleshooting and optimization. Collaborate with
Data Scientists, MLOps, and Cloud Architects
to ensure robust, compliant AI systems. Leadership & Collaboration
Lead a
small squad of engineers , providing technical guidance and fostering a high-performance culture. Mentor engineers of all levels and drive best practices in AI/ML development. Partner with
Product, Legal, and Leadership
to align AI solutions with ethical, regulatory, and business goals. Problem-Solving & Communication
Proactively resolve complex technical challenges across the AI/ML stack. Translate technical concepts for
executives, engineers, and cross-functional teams .
Required Skills & Qualifications Must-Have:
Proven experience in
GenAI architecture
(RAG, vector stores, prompt engineering). Hands-on
ML engineering
skills: model training, deployment, and production troubleshooting. Expertise in
Python
and modern software development practices. Track record of delivering
MVPs
and scalable AI solutions. Strong leadership: ability to
mentor engineers
and lead technical teams. Nice-to-Have:
Familiarity with
LLM fine-tuning
(e.g., GPT, Llama, Claude). Experience with
cloud platforms
(AWS/Azure/GCP) and MLOps tools. Knowledge of
I ethics, compliance, and governance .
We are seeking an experienced
Generative AI Solutions Architect
to lead the design and implementation of cutting-edge GenAI solutions. You will define the architecture, lead development efforts, and ensure scalable, ethical deployment of AI systems-from model selection to production. This role requires deep technical expertise in
RAG, vector stores, prompt engineering, and ML deployment , along with strong leadership to guide teams and stakeholders.
Key Responsibilities: GenAI Architecture & Development
Define end-to-end
GenAI architecture , including model selection, fine-tuning, retrieval-augmented generation (RAG), vector databases, and prompt engineering pipelines. Design and deploy
scalable software applications
to support Generative AI initiatives. Build
Minimum Viable Products (MVPs)
for rapid iteration in dynamic environments. ML Engineering & Deployment
Hands-on
model deployment
from development to production, with troubleshooting and optimization. Collaborate with
Data Scientists, MLOps, and Cloud Architects
to ensure robust, compliant AI systems. Leadership & Collaboration
Lead a
small squad of engineers , providing technical guidance and fostering a high-performance culture. Mentor engineers of all levels and drive best practices in AI/ML development. Partner with
Product, Legal, and Leadership
to align AI solutions with ethical, regulatory, and business goals. Problem-Solving & Communication
Proactively resolve complex technical challenges across the AI/ML stack. Translate technical concepts for
executives, engineers, and cross-functional teams .
Required Skills & Qualifications Must-Have:
Proven experience in
GenAI architecture
(RAG, vector stores, prompt engineering). Hands-on
ML engineering
skills: model training, deployment, and production troubleshooting. Expertise in
Python
and modern software development practices. Track record of delivering
MVPs
and scalable AI solutions. Strong leadership: ability to
mentor engineers
and lead technical teams. Nice-to-Have:
Familiarity with
LLM fine-tuning
(e.g., GPT, Llama, Claude). Experience with
cloud platforms
(AWS/Azure/GCP) and MLOps tools. Knowledge of
I ethics, compliance, and governance .