AI/ML Engineer

Spectraforce Technologies; Newark (NJ); Hybrid; USD 140,000 - 190,000; Full time

Job summary

Spectraforce Technologies is seeking a Senior AI/ML Engineer to lead GenAI deployments in Newark, NJ, with hybrid work. The candidate will bridge theory and production, building scalable, secure GenAI solutions.

Requirements include 5+ years in ML engineering, strong Python, cloud and MLOps experience, and a focus on cost-effective, reliable model serving. The role is 3 months with possible temp-to-hire.

Qualifications

• Bachelor’s degree in CS/Engineering or related field; Master’s preferred.
• 5+ years of production ML engineering experience.
• Proficiency in Python and software engineering principles.
• Experience with cloud platforms, containerization, and infrastructure management.

Responsibilities

• Deploy, monitor, and maintain GenAI models in production.
• Build and maintain data pipelines and storage for model operations.
• Utilize cloud platforms (AWS/Azure/GCP) and container orchestration (Kubernetes).
• Develop CI/CD pipelines and automate deployment processes.
• Implement secure coding, authentication, and monitoring for infrastructure and models.
• Apply GenAI techniques and RAG, with human-in-the-loop where appropriate.
• Develop agent systems using frameworks like LangChain and external APIs.

Skills

Python

GenAI

Cloud platforms

MLOps

Education

Bachelor’s degree in CS/Engineering

Master’s degree preferred

Tools

Docker

Kubernetes

Terraform

Git

Job description

Title: AI/ML Engineer

Location: Newark, NJ (Hybrid)

Duration: 3 Months (Temp to hire)

Position Overview

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.