Openkyber
SAP AI Business Services Consultant
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Senior AI/ML & MLOps Engineer on a contract basis, remote, with a pay rate of "C2H". Candidates must have 4+ years of experience in ML Engineering, expertise in Python, and familiarity with GenAI tools and cloud deployment.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
March 22, 2026
🕒 - Duration
Unknown
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🏝️ - Location
Remote
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📄 - Contract
W2 Contractor
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🔒 - Security
Unknown
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📍 - Location detailed
Alaska
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🧠 - Skills detailed
#Computer Science #DevOps #Model Optimization #Transformers #TensorFlow #Databases #Azure #SAP #Mathematics #AI (Artificial Intelligence) #Data Science #Deep Learning #Infrastructure as Code (IaC) #PyTorch #Docker #ML (Machine Learning) #AWS (Amazon Web Services) #Libraries #Automation #NumPy #Deployment #Data Quality #Langchain #MLflow #Kubernetes #Terraform #Pandas #Python #Cloud #Security #GCP (Google Cloud Platform) #Monitoring #"ETL (Extract #Transform #Load)" #Scala
Role description
Skill Matrix to be filled by Candidates: Mandatory Skills Years of Experience Year Last Used Rating Out of 10 End-to-End MLOps Automation GenAI Orchestration LLMOps Advanced Model Optimization & Inference Position Details Requirement Role Senior AI/ML & MLOps Engineer Location Remote Type of Hire - Contract/ C2H C2H Salary Range (in USD) Only W2 Job Description Role Overview We are looking for a seasoned AI/ML & MLOps Engineer to lead the development, deployment, and scaling of our machine learning initiatives. You will bridge the gap between data science and production engineering, ensuring our models ranging from traditional predictive analytics to cutting-edge Generative AI are robust, scalable, and high-performing. The ideal candidate doesn't just build models in a vacuum but builds the automated "foundries" that keep them running. Key Responsibilities
- Model Development: Design, train, and optimize ML models using frameworks like PyTorch or TensorFlow .
- GenAI Implementation: Lead the integration of LLMs, including fine-tuning, prompt engineering, and building RAG (Retrieval-Augmented Generation) pipelines.
- Infrastructure & Orchestration: Architect and maintain end-to-end ML pipelines (CI/CD for ML) using Docker , Kubernetes , and tools like MLflow or Kubeflow .
- Cloud Deployment: Deploy and manage production workloads on cloud platforms ( AWS/Google Cloud Platform/Azure ) with a focus on cost-efficiency and low latency.
- Monitoring & Governance: Implement robust monitoring for model drift, data quality, and performance metrics to ensure 24/7 reliability.
- Collaboration: Work closely with Data Scientists to productize research and with DevOps to align with enterprise security and infrastructure standards.
Technical Requirements
- Experience: 4+ years of hands-on experience in ML Engineering or MLOps roles.
- Core Stack: Expert-level proficiency in Python and standard ML libraries (Scikit-learn, Pandas, NumPy).
- Deep Learning: Strong experience with Transformers , CNNs, or RNNs.
- DevOps for ML: Mastery of containerization (Docker) and orchestration (K8s).
- Experience with Infrastructure as Code (Terraform/CloudFormation) is a major plus.
- GenAI Tools: Familiarity with LangChain, LlamaIndex, or Vector Databases (Pinecone, Milvus, Weaviate).
- Education: B.S./M.S. in Computer Science, Mathematics, or a related quantitative field.
For applications and inquiries, contact: hirings@openkyber.com
Skill Matrix to be filled by Candidates: Mandatory Skills Years of Experience Year Last Used Rating Out of 10 End-to-End MLOps Automation GenAI Orchestration LLMOps Advanced Model Optimization & Inference Position Details Requirement Role Senior AI/ML & MLOps Engineer Location Remote Type of Hire - Contract/ C2H C2H Salary Range (in USD) Only W2 Job Description Role Overview We are looking for a seasoned AI/ML & MLOps Engineer to lead the development, deployment, and scaling of our machine learning initiatives. You will bridge the gap between data science and production engineering, ensuring our models ranging from traditional predictive analytics to cutting-edge Generative AI are robust, scalable, and high-performing. The ideal candidate doesn't just build models in a vacuum but builds the automated "foundries" that keep them running. Key Responsibilities
- Model Development: Design, train, and optimize ML models using frameworks like PyTorch or TensorFlow .
- GenAI Implementation: Lead the integration of LLMs, including fine-tuning, prompt engineering, and building RAG (Retrieval-Augmented Generation) pipelines.
- Infrastructure & Orchestration: Architect and maintain end-to-end ML pipelines (CI/CD for ML) using Docker , Kubernetes , and tools like MLflow or Kubeflow .
- Cloud Deployment: Deploy and manage production workloads on cloud platforms ( AWS/Google Cloud Platform/Azure ) with a focus on cost-efficiency and low latency.
- Monitoring & Governance: Implement robust monitoring for model drift, data quality, and performance metrics to ensure 24/7 reliability.
- Collaboration: Work closely with Data Scientists to productize research and with DevOps to align with enterprise security and infrastructure standards.
Technical Requirements
- Experience: 4+ years of hands-on experience in ML Engineering or MLOps roles.
- Core Stack: Expert-level proficiency in Python and standard ML libraries (Scikit-learn, Pandas, NumPy).
- Deep Learning: Strong experience with Transformers , CNNs, or RNNs.
- DevOps for ML: Mastery of containerization (Docker) and orchestration (K8s).
- Experience with Infrastructure as Code (Terraform/CloudFormation) is a major plus.
- GenAI Tools: Familiarity with LangChain, LlamaIndex, or Vector Databases (Pinecone, Milvus, Weaviate).
- Education: B.S./M.S. in Computer Science, Mathematics, or a related quantitative field.
For applications and inquiries, contact: hirings@openkyber.com
