Job Description & Details
This is a heavy-hitting AI/ML engineering role where you will actually be shipping production-grade Generative AI and traditional ML models rather than just spinning up jupyter notebooks. If you are tired of theoretical AI discussions and want to integrate LLMs like GPT-4 and Gemini into enterprise systems like Salesforce, this is a solid career move.
What You'll Actually Be Doing
You will spend your days designing, fine-tuning, and deploying state-of-the-art machine learning models, working closely with AI architects and cloud engineers. Expect to handle the end-to-end model lifecycle, from orchestrating retraining pipelines to ensure fairness and ethics to mentoring junior engineers on MLOps best practices and system optimization.
The Core Tech Stack
You need absolute fluency in Python and modern ML frameworks, paired with deep hands-on experience deploying Large Language Models and traditional NLP architectures into cloud environments. Because this role touches enterprise CRM systems and potentially healthcare data, a strong grasp of data privacy, bias monitoring, and MLOps infrastructure is non-negotiable.
Interview Expectations
Expect the hiring team to grill you on your experience with model hallucination mitigation and fine-tuning strategies for LLMs like GPT-4 or Gemini. They are secretly testing whether you can balance cutting-edge research with the harsh realities of enterprise latency and system constraints, so be ready to walk through a production failure and how you debugged it.
Application Advice
Make sure your resume heavily features concrete metrics around the models you have deployed, specifically highlighting your work with cloud platforms, LLMs, and Python frameworks. Weave in keywords like MLOps, model fine-tuning, and cross-functional leadership to clear the ATS and show the hiring manager you operate at a senior technical level.