AI Solutions Engineer Specialist Lead

Little Rock, AR - USA

Monthly Salary: Not Disclosed

Posted on: 11 hours ago

Vacancies: 1 Vacancy

Job Summary

Job Title: AI Solutions Engineer Specialist Lead
Location: Little Rock Arkansas
Experience: 12 Years
Employment Type: Contract
Interview Type: In-Person or Webcam

Job Description

The AI Solutions Engineer Specialist Lead will be responsible for leading the design development and deployment of advanced artificial intelligence and machine learning solutions that solve complex business problems. This role involves collaborating with cross-functional teams driving strategic initiatives and ensuring scalable and efficient AI solution delivery. The ideal candidate will have deep expertise in AI engineering cloud platforms modern data architectures and leading innovation-driven projects.

Key Responsibilities

Lead the end-to-end lifecycle of AI solution development including problem definition data acquisition model development validation deployment and monitoring.
Architect and implement AI systems and MLOps pipelines for large-scale production environments.
Collaborate with product owners business leaders data scientists and engineering teams to translate business requirements into technical solutions.
Lead research on emerging AI technologies and evaluate opportunities to integrate them into enterprise solutions.
Drive automation optimization and continuous improvement of AI models and workflows.
Provide technical leadership mentorship and guidance to engineering teams and junior AI developers.
Conduct performance tuning model retraining and scalability assessments.
Ensure compliance with security privacy ethical AI and regulatory standards.
Lead proof-of-concept initiatives and present recommendations to senior stakeholders.
Document system architecture implementation patterns and operational processes.

Required Qualifications

12 years of experience in software engineering or data/AI engineering with at least 5 years focused on AI and ML system development.
Strong expertise in machine learning algorithms deep learning frameworks (TensorFlow PyTorch Keras) and classical ML tools (Scikit-learn).
Hands-on experience building production-grade AI and data pipelines using cloud platforms (AWS Azure or Google Cloud).
Proficiency in Python SQL and ML engineering tools such as MLflow Kubeflow Airflow or similar pipelines.
Experience with large-scale data processing using Spark Databricks or equivalent technologies.
Strong understanding of MLOps best practices CI/CD pipelines containerization Kubernetes and model deployment strategies.
Proven ability to architect distributed systems and data-driven applications.
Experience with NLP generative AI computer vision or LLM-based solutions.
Excellent communication skills and experience working with business stakeholders and technical teams.
Bachelors or Masters degree in Computer Science Engineering Data Science AI/ML or related field.

Preferred Skills

Experience with vector databases embeddings RAG frameworks and LLM integrations.
Hands-on experience with enterprise AI platforms such as Azure OpenAI Amazon Bedrock or Google Vertex AI.
Familiarity with model governance ethical AI implementation and responsible AI frameworks.
Knowledge of microservices architecture and API design.
Experience working in Agile development environments.
Certifications in cloud or AI engineering.

Job Title: AI Solutions Engineer Specialist Lead Location: Little Rock Arkansas Experience: 12 Years Employment Type: Contract Interview Type: In-Person or Webcam Job Description The AI Solutions Engineer Specialist Lead will be responsible for leading the design development and deployment of advanc...

Job Title: AI Solutions Engineer Specialist Lead
Location: Little Rock Arkansas
Experience: 12 Years
Employment Type: Contract
Interview Type: In-Person or Webcam

Job Description

Key Responsibilities

Lead the end-to-end lifecycle of AI solution development including problem definition data acquisition model development validation deployment and monitoring.
Architect and implement AI systems and MLOps pipelines for large-scale production environments.
Collaborate with product owners business leaders data scientists and engineering teams to translate business requirements into technical solutions.
Lead research on emerging AI technologies and evaluate opportunities to integrate them into enterprise solutions.
Drive automation optimization and continuous improvement of AI models and workflows.
Provide technical leadership mentorship and guidance to engineering teams and junior AI developers.
Conduct performance tuning model retraining and scalability assessments.
Ensure compliance with security privacy ethical AI and regulatory standards.
Lead proof-of-concept initiatives and present recommendations to senior stakeholders.
Document system architecture implementation patterns and operational processes.

Required Qualifications

12 years of experience in software engineering or data/AI engineering with at least 5 years focused on AI and ML system development.
Strong expertise in machine learning algorithms deep learning frameworks (TensorFlow PyTorch Keras) and classical ML tools (Scikit-learn).
Hands-on experience building production-grade AI and data pipelines using cloud platforms (AWS Azure or Google Cloud).
Proficiency in Python SQL and ML engineering tools such as MLflow Kubeflow Airflow or similar pipelines.
Experience with large-scale data processing using Spark Databricks or equivalent technologies.
Strong understanding of MLOps best practices CI/CD pipelines containerization Kubernetes and model deployment strategies.
Proven ability to architect distributed systems and data-driven applications.
Experience with NLP generative AI computer vision or LLM-based solutions.
Excellent communication skills and experience working with business stakeholders and technical teams.
Bachelors or Masters degree in Computer Science Engineering Data Science AI/ML or related field.

Preferred Skills

Experience with vector databases embeddings RAG frameworks and LLM integrations.
Hands-on experience with enterprise AI platforms such as Azure OpenAI Amazon Bedrock or Google Vertex AI.
Familiarity with model governance ethical AI implementation and responsible AI frameworks.
Knowledge of microservices architecture and API design.
Experience working in Agile development environments.
Certifications in cloud or AI engineering.