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Machine Learning Engineer- Gen AI

**Weekly Hours:** 40

**Role Number:** 200656735-3543

**Summary**

Product Operations partners with a variety of different engineering and operations teams, our team leads development of machine learning solutions. We deliver projects from end-to-end: problem statement and conceptualization, proof-of-concept, and participation in final deployment! You will also perform ad-hoc statistical analyses.
You will also work closely with data engineers to generate detailed business intelligence solutions. You will be expected to conduct presentations of analyses to a wide range of audiences including executives.

**Description**

Product Operations partners with a variety of different engineering and operations teams, our team leads development of machine learning solutions. We deliver projects from end-to-end: problem statement and conceptualization, proof-of-concept, and participation in final deployment!

You will also perform ad-hoc statistical analyses. You will also work closely with data engineers to generate detailed business intelligence solutions. You will be expected to conduct presentations of analyses to a wide range of audiences including executives.

**Minimum Qualifications**

+ 3+ years experience in GenAI applications, machine learning algorithms, software engineering, and data mining models with an emphasis on large language models (LLM) or large multimodal models (LMM).

+ Masters in Artificial intelligence, Machine Learning, Computer Science, Statistics, Operations Research, Physics, Mechanical Engineering, Electrical Engineering or related field.

**Preferred Qualifications**

+ Proven experience in GenAI application building with agents and agentic workflows. Experience with LLM and LMM development and fine-tuning is a major plus.

+ Proficiency in using cutting-edge GenAI tools, i.e. Claude Code, Roo Code, etc.

+ Familiarity with distributed computing, cloud infrastructure, and orchestration tools, such as Kubernetes, Apache Airflow (DAG), Docker, Conductor, Ray for LLM training and inference at scale is a plus.

+ Hands-on experience with LangChain and LlamaIndex, enabling RAG applications and LLM orchestration.

+ Ability to meaningfully present results of analyses in a clear and impactful manner, breaking down complex ML/LLM concepts for non-technical audiences.

+ Experience applying ML techniques in manufacturing, testing, or hardware optimization is a major plus.

+ Proven experience in leading and mentoring teams is a plus.


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