Skill Matrix to be filled by Candidates:
| Mandatory Skills | Years of Experience | Year Last Used | Rating Out of 10 |
|
End-to-End MLOps Automation |
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| GenAI Orchestration |
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| LLMOps |
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| Advanced Model Optimization & Inference |
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| 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 modelsranging from traditional predictive analytics to cutting-edge Generative AIare robust scalable and high-performing. The ideal candidate doesnt 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/GCP/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.
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| | | | | |
Required Skills:
MLOpsLLMLLMOps
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 P...
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 modelsranging from traditional predictive analytics to cutting-edge Generative AIare robust scalable and high-performing. The ideal candidate doesnt 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/GCP/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.
|
| | | | | |
Required Skills:
MLOpsLLMLLMOps
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Job Location:
Monthly Salary:
Not Disclosed
Posted on:
2 hours ago
Vacancies:
1 Vacancy
