Lead development of Agentic AI capabilities and LLM applications for multiple mission management applications. Mentor teams to implement ML algorithms addressing customer challenges.
About the role
- Senior Lead AI/ML Engineer focused on data-driven solutions in the AI & Data Engineering sector. Developing predictive models and collaborating with cross-functional teams in Chicago, IL.
Responsibilities
- Partner with product and business teams to define problems and translate them into data-driven solutions.
- Conduct exploratory data analysis (EDA) and extract actionable insights from structured and unstructured datasets.
- Develop, validate, and iterate on predictive models using techniques in supervised, unsupervised, and/or time series learning.
- Communicate modeling outcomes through clear visualizations and presentations to both technical and non-technical stakeholders.
- Build and maintain robust pipelines for model training, evaluation, and inference.
- Deploy machine learning models into production with attention to scalability, performance, and observability.
- Monitor model drift and performance over time and develop retraining and versioning strategies.
- Collaborate with software and data engineering teams to integrate ML solutions into end-user applications and internal systems.
Requirements
- Master’s plus degree in Computer Science, Statistics, Applied Mathematics, or a related field.
- 5+ years of experience in data science and machine learning, with a proven track record of delivering models to production.
- Proficiency in Python and ML libraries such as scikit-learn, XGBoost, LightGBM, PyTorch, or TensorFlow.
- Strong understanding of statistical modeling, machine learning algorithms, and experiment design.
- Solid experience with SQL and data manipulation tools (e.g., Pandas, Spark, or Dask).
- Experience deploying models using APIs (Flask, FastAPI), Docker, and orchestration tools (e.g., Airflow, Kubeflow, MLflow).
- Hands-on experience with cloud platforms (AWS, GCP, or Azure) and model serving tools.
- Excellent problem-solving and communication skills; able to explain complex concepts clearly and effectively.
- Experience with time series forecasting, causal inference, recommendation systems, or NLP (preferred).
- Familiarity with data versioning and reproducibility tools (e.g., DVC, Weights & Biases) (preferred).
- Exposure to feature stores, streaming data (e.g., Kafka), or real-time ML systems (preferred).
- Background in MLOps and experience building generalizable ML frameworks or platforms (preferred).
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