AI / ML Engineer (Generative AI)

JSR Tech Consulting; Newark (NJ); Hybrid; USD 96,000 - 117,000; Full time

Job summary

JSR Tech Consulting is seeking a Senior Machine Learning Engineer to advance GenAI initiatives in production. You will bridge theory and practical deployment, ensuring scalable, secure GenAI solutions with cost-conscious designs.

You will leverage Python, cloud platforms, containers, and CI/CD to drive reliable model delivery, while exploring LLMs, RAG, and agentic architectures in a fast-paced, collaborative environment.

Qualifications

• 5+ years of production ML engineering experience.
• Strong Python and software engineering fundamentals.
• Experience deploying GenAI models and pipelines.
• Proficiency with cloud platforms (AWS/Azure/GCP).
• Familiarity with DevOps automation tools.

Responsibilities

• Deploy, monitor, and maintain GenAI models in production.
• Build data pipelines and storage for model operations.
• Use cloud platforms for deployment and orchestration.
• Develop CI/CD pipelines and manage version control.
• Implement secure coding, auth, and monitoring.
• Apply GenAI frameworks and prompt engineering.
• Work with LangChain and multi-agent systems.
• Optimize costs of GenAI deployments.

Skills

Python

GenAI

Model deployment

Cloud platforms

Docker

Kubernetes

Terraform/CloudFormation

LLMs

Prompt engineering

Development

DevOps

Education

Bachelor's degree in CS/Engineering/Data Science

MS/PhD preferred

Tools

Docker

Kubernetes

Terraform/CloudFormation

Git

Job description

Location: Hybrid - Newark, NJ

Employment Type: Right to Hire (Contract-to-Hire)

Pay Rate: $70-$85/hr

Position Overview

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, ensuring our GenAI solutions are innovative, reliable, scalable, secure, and cost-effective.

This is a right-to-hire position requiring permanent U.S. work authorization Sponsorship is not available. Recent MS or PhD graduates are encouraged to apply.

Key Responsibilities

• Deploy, monitor, and maintain GenAI models in production, ensuring reliable performance in real-world settings
• Build and maintain efficient data pipelines and storage solutions that support model operations
• Utilize cloud platforms (AWS, Azure, GCP) for model deployment, containerization (Docker), orchestration (Kubernetes), and infrastructure as code (Terraform/CloudFormation)
• Develop CI/CD pipelines, manage version control (Git), and automate deployment processes
• Implement secure coding practices, authentication, authorization, and robust monitoring and alerting for infrastructure and model performance
• Apply deep expertise in LLMs, GenAI architectures, frameworks like Hugging Face, prompt engineering, and specialized GenAI infrastructure
• Apply advanced techniques such as Retrieval-Augmented Generation (RAG), hallucination monitoring, and human-in-the-loop systems
• Design and develop agent and multi-agent systems using frameworks like LangChain, enabling interaction with external APIs and tools
• Implement strategies to manage and reduce operational costs associated with GenAI deployments

Qualifications

• Bachelor's degree in Computer Science, Engineering, Data Science, or a related field; Master's degree preferred
• 5+ years of 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 developing agentic systems and multi-agent architectures
• Proven track record of cost optimization in AI deployments
• Comfortable working in a fast-paced environment as an independent contributor