Data Analyst supporting supply chain risk management for federal agencies. Focusing on data analysis and mitigation strategies in a collaborative team environment.
About the role
- Lead AI Engineer focused on developing cutting-edge AI/ML solutions tailored for after-sales operations. Guiding a team of AI engineers from ideation to deployment and continuous improvement.
Responsibilities
- Lead, mentor, a team of AI engineers, fostering a collaborative and innovative environment.
- Provide technical guidance and oversight throughout the AI solution development lifecycle.
- Drive best practices in MLOps, model development, testing, and deployment.
- Stay abreast of the latest advancements in AI/ML and recommend their application to relevant business challenges.
- Design, build, and deploy end-to-end AI/ML models and systems that address critical after-sales service needs.
- Develop robust, scalable, and production-ready code for AI applications.
- Perform data exploration, feature engineering, model training, evaluation, and optimization.
- Integrate AI solutions with existing enterprise systems and data pipelines.
- Collaborate closely with product managers, data scientists, business stakeholders, and IT teams to understand requirements, define project scope, and deliver impactful AI solutions.
- Translate complex business problems into well-defined AI/ML technical requirements and architectural designs.
- Identify opportunities to leverage AI to improve efficiency, reduce costs, and enhance customer satisfaction in after-sales service.
- Debug and troubleshoot complex AI systems and data issues.
Requirements
- Doctorate / Bachelor's or Master's degree in Computer Science, Engineering, :Artificial Intelligence, Machine Learning, Data Science, or a related quantitative field.
- 5+ years of professional experience in Artificial Intelligence, Machine Learning, or Data Science roles.
- 2+ years of experience leading or mentoring a small team of engineers or data scientists.
- Strong hands-on programming proficiency in Python (including libraries like TensorFlow, PyTorch, scikit-learn, pandas, numpy).
- Demonstrated experience in designing, building, and deploying machine learning models into production environments.
- Solid understanding of machine learning algorithms (e.g., supervised, unsupervised, reinforcement learning, deep learning) and their practical applications.
- Experience with Natural Language Processing (NLP)/LLM/Gen AI techniques and tools.
- Experience with cloud platforms (e.g., GCP, AWS, Azure) for AI/ML development and deployment.
- Experience with MLOps practices and tools (e.g., MLflow, Kubeflow, Docker, Kubernetes).
- Knowledge of causal inference, reinforcement learning, or optimization techniques.
- Excellent problem-solving skills and the ability to work independently and as part of a team.
- Strong communication and interpersonal skills, with the ability to explain complex technical concepts to non-technical stakeholders.
- Familiarity with after-sales service operations, customer support systems, or automotive industry data.
- Contributions to open-source projects or a strong portfolio of personal AI/ML projects.
Job title
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