> Markdown version of [/jobs/ext/1395032-ai-ml-engineer](https://www.wearedevelopers.com/jobs/ext/1395032-ai-ml-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI/ML Engineer - **Company:** Insight Global - **Location:** Buffalo Grove, IL, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Continuous Integration, Software Design Documents, Python (Programming Language), Machine Learning, NumPy, Cloud Services, Tensorflow, Software Engineering, Feature Engineering, Pytorch, Large Language Models, Multi-Agent Systems, Deep Learning, Fastapi, Pandas, Scikit Learn, Xgboost, Machine Learning Operations, GPT, Data Pipelines, Microservices - **Published:** July 23, 2026 - **Apply:** https://dejobs.org/x/x/6749A6CA66DD4BC6B598A7CD6E5E694A/job/ ## About the Role * 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 ## Description Own end-to-end ML/AI projects: problem framing, data pipelines, modeling, offline/online evals, deployment, monitoring, and iteration. * 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 ## Related Videos - [ Evaluating AI models for code comprehension](https://www.wearedevelopers.com/videos/1462-evaluating-ai-models-for-code-comprehension) - [Vectorize all the things! 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