Matlen Silver
This range is provided by Matlen Silver. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.
Base pay range
$65.00/hr - $85.00/hr About the Role
We are seeking an
AI Solutions Engineer
to design, build, and deploy intelligent systems that solve complex business problems. This role bridges data science, software engineering, and solution architecture — bringing machine learning and generative AI technologies into production environments that deliver measurable impact. The ideal candidate combines technical depth in AI/ML systems with strong communication skills to collaborate across data, product, and engineering teams. Key Responsibilities
Design and implement end-to-end AI solutions — from data ingestion and model training to deployment and monitoring. Collaborate with cross-functional teams to identify opportunities where AI can drive business value. Build, fine-tune, and integrate machine learning and generative AI models (LLMs, NLP, computer vision, etc.). Develop APIs, pipelines, and infrastructure to support scalable AI deployments. Partner with data engineers and cloud architects to ensure model reliability, security, and performance. Evaluate and integrate third-party AI tools, APIs, and frameworks. Create documentation, dashboards, and demos to communicate results to both technical and non-technical audiences. Stay up to date on emerging AI technologies, frameworks, and responsible AI practices. Qualifications
Required
Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or related field. 3+ years of experience in software development or applied AI/ML engineering. Proficiency in
Python ,
SQL , and machine learning frameworks (TensorFlow, PyTorch, scikit-learn). Experience with
cloud platforms
such as AWS, Azure, or GCP. Strong understanding of APIs, microservices, and modern DevOps practices. Hands‑on experience deploying AI models in production (e.g., via Docker, Kubernetes, or MLflow). Excellent problem‑solving, communication, and collaboration skills. Preferred
Experience working with
LLMs ,
vector databases , or
retrieval‑augmented generation (RAG)
systems. Familiarity with
MLOps
and model lifecycle management. Exposure to data pipelines (Airflow, Databricks, or Spark). Background in data visualization and communicating technical insights to business stakeholders. What You’ll Bring
A passion for building real‑world AI systems that make an impact. Curiosity to experiment and innovate responsibly.
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$65.00/hr - $85.00/hr About the Role
We are seeking an
AI Solutions Engineer
to design, build, and deploy intelligent systems that solve complex business problems. This role bridges data science, software engineering, and solution architecture — bringing machine learning and generative AI technologies into production environments that deliver measurable impact. The ideal candidate combines technical depth in AI/ML systems with strong communication skills to collaborate across data, product, and engineering teams. Key Responsibilities
Design and implement end-to-end AI solutions — from data ingestion and model training to deployment and monitoring. Collaborate with cross-functional teams to identify opportunities where AI can drive business value. Build, fine-tune, and integrate machine learning and generative AI models (LLMs, NLP, computer vision, etc.). Develop APIs, pipelines, and infrastructure to support scalable AI deployments. Partner with data engineers and cloud architects to ensure model reliability, security, and performance. Evaluate and integrate third-party AI tools, APIs, and frameworks. Create documentation, dashboards, and demos to communicate results to both technical and non-technical audiences. Stay up to date on emerging AI technologies, frameworks, and responsible AI practices. Qualifications
Required
Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or related field. 3+ years of experience in software development or applied AI/ML engineering. Proficiency in
Python ,
SQL , and machine learning frameworks (TensorFlow, PyTorch, scikit-learn). Experience with
cloud platforms
such as AWS, Azure, or GCP. Strong understanding of APIs, microservices, and modern DevOps practices. Hands‑on experience deploying AI models in production (e.g., via Docker, Kubernetes, or MLflow). Excellent problem‑solving, communication, and collaboration skills. Preferred
Experience working with
LLMs ,
vector databases , or
retrieval‑augmented generation (RAG)
systems. Familiarity with
MLOps
and model lifecycle management. Exposure to data pipelines (Airflow, Databricks, or Spark). Background in data visualization and communicating technical insights to business stakeholders. What You’ll Bring
A passion for building real‑world AI systems that make an impact. Curiosity to experiment and innovate responsibly.
#J-18808-Ljbffr