Job Description & Details
This is a heavy-hitting AI/ML engineering role focused on building and scaling generative AI, NLP, and computer vision models for production. If you enjoy moving past the proof-of-concept phase and want to deal with actual MLOps pipelines and LLM deployments at scale, this contract gives you a solid platform to do it.
What You'll Actually Be Doing
Your day-to-day will be a mix of wrangling messy enterprise data, fine-tuning machine learning models, and building robust RAG pipelines using modern frameworks like LangChain. You'll be collaborating directly with product and analytics teams to understand business requirements, translating those into functional AI solutions, and deploying them onto cloud infrastructure without breaking production.
The Core Tech Stack
You need to be completely fluent in Python and have deep, hands-on experience with frameworks like PyTorch or TensorFlow for traditional ML, alongside modern LLM tooling such as OpenAI APIs, LangChain, and RAG architectures. They are also looking for strong cloud chops in AWS, Azure, or GCP, plus the standard engineering toolset of Docker, Git, SQL, and REST APIs to make sure your models actually play nicely with downstream systems.
Interview Expectations
Expect the hiring team to grill you on system design for generative AI, specifically asking how you would scale a RAG application to handle high-throughput enterprise queries while minimizing latency and hallucinations. They are looking to see if you understand the operational bottlenecks of LLMs in production, not just how to write a script that calls an API. You should also be prepared for a deep dive into your past experience with feature engineering and how you handle data drift over time.
Application Advice
Make sure your resume doesn't just list frameworks, but explicitly highlights end-to-end ML systems you have built and deployed into production. Clearly feature your hands-on experience with LangChain, vector databases, and cloud platforms like AWS or Azure right at the top, as the ATS and the hiring manager will be hunting for these exact keywords to filter out candidates who only have theoretical knowledge.