Senior Machine Learning Engineer (NLP/LLM)

Senior Machine Learning Engineer (NLP/LLM)

Why Join Tech9?

At Tech9, we are driven by a clear vision—to empower organizations with AI-centered solutions that make them more adaptable, efficient, and future-ready. As a company at the forefront of innovation, we help our clients build exceptional software that not only meets today’s needs but anticipates tomorrow's challenges. Our approach blends cutting-edge AI technology, top-tier talent acquisition, and expert project management to ensure that businesses can scale effectively and deliver high-quality, world-class software on time and within budget.

Our partnerships speak volumes, with clients like Instructure, Young Living, Imagine Learning, Mars Corp., and many others trusting us to lead the way in software development. We are rapidly growing across our offices in the US, LATAM, and India, and we're creating an environment where talented individuals can thrive, collaborate, and have fun while building transformative solutions.

If you're excited by the opportunity to work in a fast-paced, innovative environment where scaling and building the future of software is key, we’d love to hear from you. Join us as we work together to redefine the world of software development!

Project Overview

Our client is a fast-paced startup building ML-powered threat detection systems that identify and neutralize digital disinformation at scale for Fortune 1000 companies and government agencies. Their core platform runs on sophisticated NLP models that process millions of data points in real-time, using advanced classification algorithms and anomaly detection to separate legitimate content from coordinated threats.

As an ML Engineer on this team, you'll architect and implement the machine learning backbone that powers their detection capabilities. You'll work directly with production-grade language models, building everything from content embedding systems to behavioral propensity models that predict threat patterns before they manifest. The technical stack centers on OpenAI's LLMs integrated through LangChain pipelines, with Ray handling distributed model inference and MLflow managing the complete model lifecycle from experimentation to deployment.

Your models will need to operate in resource-constrained environments, requiring expertise in model compression, quantization, and embedded deployment strategies. You'll build supervised classifiers for known threat patterns while simultaneously developing unsupervised clustering algorithms to detect novel attack vectors. The propensity modeling component involves predicting user behavior, content virality, and threat escalation probability across multiple channels.

This isn't just another ML role—you're building the intelligence layer that protects democratic institutions and major brands from sophisticated information warfare. Your algorithms will need to distinguish between genuine grassroots movements and artificial amplification, requiring deep understanding of both technical ML fundamentals and the nuanced behavioral patterns that separate authentic from synthetic content.

The impact is immediate and measurable: your models directly influence real-world security decisions, making this an ideal environment for an ML engineer ready to see their work defend against evolving digital threats while mastering production-scale NLP systems.

Model Development & Implementation

• Build and fine-tune NLP models for content classification, anomaly detection, and threat identification
• Implement propensity models across supervised/unsupervised learning paradigms
• Deploy embedded models for real-time inference and edge computing scenarios

Production ML & Collaboration

• Work with senior engineers to productionize models using LangChain, Ray APIs, and MLflow
• Contribute to ML pipelines integrating OpenAI LLMs for content analysis and classification
• Collaborate with engineering teams to optimize model performance and scalability
   

• Programming: Python (Pandas, Scikit-learn, PyTorch/TensorFlow)
• NLP/LLM: OpenAI APIs, LangChain, Hugging Face Transformers
• ML Ops: MLflow, Ray, Docker for model deployment
• Embedded ML: TensorFlow Lite, ONNX for edge deployment
   

• 5+ years of of professional experience as a Machine Learning Engineer, Data Engineer, or Data Scientist
• 3+  years hands-on experience with NLP/LLM development and deployment
• Strong understanding of ML fundamentals across supervised/unsupervised learning
• Strong competency with MLFlow and LangChain
• 3+ years of experience with embedded models and deployment optimization
• Practical knowledge of propensity modeling and statistical analysis
• Excellent proficiency in Python and ML frameworks
• C1+ English proficiency
• Based in LATAM (Mexico, Costa Rica, Colombia)

• Master's Degree in Artificial Intelligence or equivalent

At Tech9, we are committed to providing a smooth, efficient, and transparent candidate experience. Our goal is to move quickly through the interview process, typically completing it within 2-3 weeks, depending on availability. We want to make sure you have clarity on every step, and we will keep you informed about the next steps as we progress. The desired start date for this position is end of August and we aim to complete the process well before then.

Interview Plan:

1. Screening Interview (On-Demand HireVue)
   Duration: 15-30 minutes
   Format: Online assessment where we will gauge your initial qualifications and experience.
    
2. Recruiter Q&A
   Duration: 10 minutes
   Format: Virtual discussion with our recruiter to address any initial questions and go over the job details.
    
3. Round 1: Take Home Assessment
   Duration: 1.5 - 2 hours
   Format: Take home assessment to gauge NLP and LLM creativity and skills.
    
4. Hiring Manager Interview
   Duration: 15-30 minutes
   Format: Virtual interview with the hiring manager to discuss the role in more detail, evaluate cultural fit, and review your experience.
    
5. Client Interview 1
   Duration: 45 minutes
   Format: Virtual interview with a client manager to assess how your skills and experience align with the client’s needs and expectations.

1. Client Interview 2
   Duration: 45 minutes
   Format: Virtual interview with a client representative to assess how your skills and experience align with the client’s needs and expectations.
    

Next Steps:
We aim to finalize decisions and extend offers within a few days after the final round of interviews, ensuring a swift and transparent process. Our goal is to have you ready to start end of August

We look forward to getting to know you better and moving quickly through this process to bring you on board as part of the Tech9 team!