Lead Machine Learning Engineer (Generative AI Focus)

XFORIA Inc; Town of Newark (WI); Hybrid; USD 150,000 - 210,000; Full time

Job summary

XFORIA Inc. is seeking a Senior Lead Machine Learning Engineer focused on Generative AI to bridge theory and production. You will deploy, monitor, and optimize GenAI models, craft scalable data pipelines, and manage cloud-based infrastructure with containerization and IaC.

You will work with LangChain, RAG, and agent architectures, ensuring secure, cost-efficient deployments in a fast-paced environment.

Qualifications

• Bachelor's degree in CS/Engineering or related field (Master's preferred).
• 5+ years' experience deploying ML models in production.
• Strong Python and software engineering fundamentals.
• Experience with cloud platforms, containers, and infrastructure management.
• GenAI frameworks, prompt engineering, and model serving expertise.
• Ability to manage GPU/TPU resources and optimize serving frameworks.

Responsibilities

• Deploy, monitor, and maintain GenAI models in production.
• Build and maintain data pipelines and storage for model ops.
• Utilize cloud platforms (AWS, Azure, GCP) with Docker, Kubernetes, and IaC tools.
• Develop CI/CD pipelines and manage Git-based workflows.
• Implement secure coding practices and robust monitoring/alerting.

Skills

Python

GenAI frameworks

Prompt engineering

Model serving

Cloud platform experience

CI/CD

LangChain

GPU/TPU management

Education

Bachelor's degree in CS/Engineering

Master's degree preferred

Tools

Docker

Kubernetes

Git

Terraform/CloudFormation

Hugging Face

Job description

Lead Machine Learning Engineer (Generative AI Focus)

Location - Newark, NJ

3 Days Hybrid every week.

F2F interview is mandate.

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, Google Cloud Platform) 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