Design and implement machine learning models for anomaly detection in time series and behavioral data.
Develop and maintain NLP pipelines for document processing and content generation.
Preprocess and clean structured and unstructured data using standard techniques.
Implement vectorization techniques and integrate with vector databases (e.g., FAISS, Pinecone, MongoDB Atlas Vector).
Work with embedding models (e.g., OpenAI, Hugging Face) to support semantic search and retrieval tasks.
Fine-tune and evaluate LLMs for specific use cases such as summarization, classification, and test case generation.
Collaborate with backend engineers to expose ML models via APIs.
Monitor model performance using metrics like precision, recall, F1 score, and ROC-AUC.
Contribute to proof-of-concept projects involving GenAI and RAG architectures.
Follow Responsible AI practices in model development and deployment.
We are a company committed to creating inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity employer that believes everyone matters. Qualified candidates will receive consideration for employment opportunities without regard to race, religion, sex, age, marital status, national origin, sexual orientation, citizenship status, disability, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to
Human Resources Request Form. The EEOC "Know Your Rights" Poster is available
here.
To learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy:
https://insightglobal.com/workforce-privacy-policy/ .
4+ years of experience in machine learning or data science roles.
Proficiency in Python and common ML/NLP libraries (e.g., scikit-learn, spaCy, Hugging Face Transformers).
Hands-on experience with anomaly detection techniques such as Isolation Forest, One-Class SVM, Autoencoders, or statistical methods.
Familiarity with NLP tasks such as classification, summarization, and named entity recognition.
Experience with vectorization techniques (TF-IDF, Word2Vec, BERT, etc.).
Experience with vector databases (e.g., FAISS, Pinecone, ChromaDB).
Exposure to LLMs and prompt engineering.
Basic understanding of MLOps concepts and model deployment practices.
3 + years of experience building, scaling, with anomaly detection techniques such as with time series modeling and anomaly detection in production environments
Experience with LangChain or Retrieval-Augmented Generation (RAG) pipelines
Familiarity with embedding strategies and chunking techniques
Exposure to LLMOps tools and frameworks
Understanding of Responsible AI principles and ethical AI development
Benefit packages for this role will start on the 31st day of employment and include medical, dental, and vision insurance, as well as HSA, FSA, and DCFSA account options, and 401k retirement account access with employer matching. Employees in this role are also entitled to paid sick leave and/or other paid time off as provided by applicable law.