AI/ML Engineer
Job
Insight Global, LLC
Buffalo Grove, IL (In Person)
Full-Time
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Job Description
at Insight Global, LLC in Buffalo Grove, Illinois, United States Job Description Job Description Own end-to-end ML/AI projects: problem framing, data pipelines, modeling, offline/online evals, deployment, monitoring, and iteration.
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- Build and productionize agentic workflows (tool-using/multi-step agents with retrieval, planning, and human-in-the-loop), including safety/guardrails and reliability.
- Train classical ML models (tree ensembles, linear models, anomaly detectors, time-series forecasting) and deep learning models when appropriate.
- Operationalize models with CI/CD, feature stores, reproducible training, and model registries
- Monitor and improve live systems: data & concept drift detection, performance regression, bias/fairness, cost/latency; drive remediation playbooks.
- Partner cross-functionally with product, data, and platform teams; write clear design docs
- Work as an Individual contributor with minimal directions
- Should be able to interact with stakeholders, understand the problem statement, and come up with solutions We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day.
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Insight Global's Workforce Privacy Policy:
https://insightglobal.com/workforce-privacy-policy/. Skills and Requirements- Minimum 6+ years hands-on ML engineering (with significant ML experience prior to 2023): you've shipped multiple ML systems to production. Not looking for someone who started working on AI after pre-trained models/ChatGPT were released
- Minimum 12+ years of total experience in Software Development, preferably with a Data Analyst/Data Scientist background
- Demonstrated production agent build (at least one end-to-end agentic framework delivered to users).
- Strong with classical
ML:
feature engineering, cross-validation, calibration, regularization, class imbalance, interpretability ( SHAP / LIME ), time-series (forecasting, seasonality, drift).- Solid deep learning foundations ( CNN / RNN /Transformers), and practical fine-tuning experience (e.g., LoRA/QLoRA, instruction tuning, RAG ).
Proven MLOps:
model registry/experiment tracking (MLflow or equivalent), model serving (FastAPI/TF-Serving/TorchServe/ TGI /vLLM), observability.- Fluency in Python and the ML stack (NumPy/Pandas, scikit-learn, XGBoost/LightGBM, PyTorch/TensorFlow).
- Excellent communication; can drive projects independently as an Individual Contributor.
- Must have
- Experience with agent frameworks (LangGraph/LangChain Agents, AutoGen, Google ADK ) and tool use (function calling, tool routing, planners).
- Retrieval/ RAG design: chunking strategies, embedding models, vector stores ( FAISS , Pinecone, Weaviate), hybrid search, evals.
- Must have implemented projects involving some classical ML problems(classification, clustering, regression, anomaly detection, time series etc.,)
- Some working experience with Cloud services, CI/CD and Microservices
- Experience leading/mentoring junior data scientists or ML engineers is a plus
- Experience fine tuning SLMs is a huge plus To view full details and how to apply, please login or create a Job Seeker account
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