Sr Machine Learning Engineer

Must Haves:

RAG (Retrieval -Augmented Generation) and production level coding experience

RAG gives the model the right information. Production‑level coding ensures the whole system works reliably at scale.

Extensive ML engineering experience, including 5+ years of Python/SQL development and 3+ years deploying NLP/deep learning models in cloud production environments.

Advanced GenAI and LLM expertise, including RAG workflows, model optimization (LoRA/QLoRA, PEFT/SFT), vector databases, and frameworks like PyTorch/TensorFlow and LangChain.

Strong production ‑ grade engineering skills, covering ML pipeline automation, monitoring, guardrails, bias mitigation, CI/CD, and best ‑ practice software engineering.

Title: Sr Machine Learning Engineer, GenAI

Location: Boston MA

Onsite Requirements: Remote

Interview – 2 rounds and an assignment

Job Description

Our client is seeking a Sr. Machine Learning Engineer to lead the development and optimization of generative AI applications and production‑grade machine learning systems. This role will architect and scale a centralized GenAI platform, operationalize advanced models, and establish best‑practice frameworks, guardrails, and workflows for safe, high‑impact AI deployment. The ideal candidate brings deep expertise across the ML lifecycle, modern GenAI techniques, and production engineering, while also mentoring teams and driving ethical, scalable AI innovation across the organization.

Responsibilities/Must Haves

• Extensive ML engineering experience, including 5+ years of Python/SQL development and 3+ years deploying NLP/deep learning models in cloud production environments.

• Advanced GenAI and LLM expertise, including RAG workflows, model optimization (LoRA/QLoRA, PEFT/SFT), vector databases, and frameworks like PyTorch/TensorFlow and LangChain.

• Strong production‑grade engineering skills, covering ML pipeline automation, monitoring, guardrails, bias mitigation, CI/CD, and best‑practice software engineering.