Job description

Remote position: Requisition for Lead Machine Learning Engineer (Generative AI Focus) We are seeking a highly skilled and experienced Senior Machine Learning Engineer to join our dynamic team. In the rapidly evolving world of Generative AI (GenAI), this role demands not only traditional machine learning expertise but also a deep understanding of GenAI-specific challenges. The ideal candidate will be a pivotal bridge between the theoretical capabilities of GenAI models and their practical application in production environments. We are looking for someone who can ensure our GenAI solutions are innovative, reliable, scalable, secure, and cost-effective. Key Responsibilities: •Model Deployment & Maintenance: Focus on deploying, monitoring, and maintaining GenAI models in production, ensuring they function reliably in real-world settings. •Data Engineering: Build and maintain efficient data pipelines and storage solutions that support model operations. •Infrastructure Management: Utilize cloud platforms (AWS, Azure, GCP) for model deployment, containerization (Docker), orchestration (Kubernetes), and infrastructure as code (Terraform/CloudFormation). •DevOps & Automation: Develop CI/CD pipelines, manage version control (Git), and automate deployment processes for seamless operational efficiency. •Security & Monitoring: Implement secure coding practices, authentication, authorization, and set up robust monitoring and alerting systems for both infrastructure and model performance. •Generative AI Expertise: Deep understanding of LLMs, GenAI architectures, frameworks like Hugging Face, prompt engineering, and specialized infrastructure for GenAI workloads. •Advanced Techniques: Apply advanced GenAI techniques like Retrieval-Augmented Generation (RAG), hallucination monitoring, and human-in-the-loop systems. •Agent Development: Design and develop agent and multi-agent systems using frameworks like LangChain, enabling them to interact with external APIs and tools efficiently. •Cost Optimization: Implement strategies to manage and reduce the operational costs associated with GenAI deployments. Qualifications: •Bachelor's degree in computer science/Engineering, data science, or a related field. Master's degree preferred •At least five plus years' experience as a machine learning engineer, deploying models in production •Strong proficiency in Python and software engineering principles. •Solid understanding of machine learning fundamentals and model lifecycle management. •Experience with cloud platforms, containerization, and infrastructure management. •Familiarity with DevOps practices and automation tools. •Expertise in GenAI frameworks, prompt engineering, and model serving. •Ability to manage GPU/TPU resources and optimize model serving frameworks. •Experience in developing agentic systems and multi-agent architectures. •Proven track record in cost optimization in AI deployments. •Experience working in fast paced environment and independent worker Impact & Purpose: We are committed to attracting the best and brightest talent who are driven by impact and purpose. The Senior Machine Learning Engineer will play a crucial role in advancing our GenAI capabilities, pushing the boundaries of innovation while ensuring practical application and scalability. If you are passionate about transforming theoretical AI models into impactful real-world solutions, we invite you to join our team. #LI-AR1

Requirements

Must-haves

Requisition for Lead Machine Learning Engineer (Generative AI Focus)
•Model Deployment & Maintenance: Focus on deploying, monitoring, and maintaining GenAI models in production, ensuring they function reliably in real-world settings.
•Data Engineering: Build and maintain efficient data pipelines and storage solutions that support model operations.
•Infrastructure Management: Utilize cloud platforms (AWS, Azure, GCP) for model deployment, containerization (Docker), orchestration (Kubernetes), and infrastructure as code (Terraform/CloudFormation).
•DevOps & Automation: Develop CI/CD pipelines, manage version control (Git), and automate deployment processes for seamless operational efficiency.
•Security & Monitoring: Implement secure coding practices, authentication, authorization, and set up robust monitoring and alerting systems for both infrastructure and model performance.
•Generative AI Expertise: Deep understanding of LLMs, GenAI architectures, frameworks like Hugging Face, prompt engineering, and specialized infrastructure for GenAI workloads.
•Advanced Techniques: Apply advanced GenAI techniques like Retrieval-Augmented Generation (RAG), hallucination monitoring, and human-in-the-loop systems.
•Agent Development: Design and develop agent and multi-agent systems using frameworks like LangChain, enabling them to interact with external APIs and tools efficiently.
•Cost Optimization: Implement strategies to manage and reduce the operational costs associated with GenAI deployments.
•At least five plus years' experience as a machine learning engineer, deploying models in production
•Strong proficiency in Python and software engineering principles.
•Solid understanding of machine learning fundamentals and model lifecycle management.
•Experience with cloud platforms, containerization, and infrastructure management.
•Familiarity with DevOps practices and automation tools.
•Expertise in GenAI frameworks, prompt engineering, and model serving.
•Ability to manage GPU/TPU resources and optimize model serving frameworks.
•Experience in developing agentic systems and multi-agent architectures.
•Proven track record in cost optimization in AI deployments.
•Experience working in fast paced environment and independent worker
Impact & Purpose: We are committed to attracting the best and brightest talent who are driven by impact

Nice-to-haves

•Bachelor's degree in computer science/Engineering, data science, or a related field. Master's degree preferred

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningaiawsazuregcpdockerkubernetesci/cdpython

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Effort: medium