AI Technical Lead Engineer
| Company | Everest |
|---|---|
| Location | Middlesex, NJ, USA |
| Salary | $Not Provided – $Not Provided |
| Type | Full-Time |
| Degrees | |
| Experience Level | Mid Level, Senior |
Requirements
- Proven expertise in building AI solutions that can operate autonomously or semi-autonomously (e.g., reinforcement learning, multi-agent systems).
- In-depth knowledge of ML algorithms, feature engineering, and data preprocessing techniques.
- Experience integrating machine learning frameworks (e.g., TensorFlow, PyTorch) into production environments.
- Advanced proficiency in SQL for database querying and Python for AI model development, data manipulation, and automation.
- Strong experience in designing and implementing ETL pipelines, particularly with Airflow or similar orchestration tools.
- Familiarity with Azure or Google Cloud for data storage, processing, and AI deployments.
- Knowledge of Databricks, Apache Spark, Hadoop, or similar technologies for large-scale data processing.
- Ability to translate complex AI concepts into actionable roadmaps, guiding junior team members and stakeholders.
Responsibilities
- Define and execute AI strategy, focusing on innovative, agentic AI workflows that enable autonomous or semi-autonomous decision-making.
- Collaborate with leadership and cross-functional teams to align AI initiatives with business objectives.
- Oversee the full AI lifecycle—from data ingestion and model training to deployment and monitoring—to ensure high-impact, scalable solutions.
- Continuously refine AI workflows, leveraging best practices in model iteration and optimization.
- Design, prototype, and implement AI agents that can learn, adapt, and interact with users or systems with minimal human intervention.
- Utilize reinforcement learning, advanced machine learning techniques, and robust data pipelines to power autonomous or semi-autonomous AI systems.
- Build and maintain scalable data architectures in support of AI models, ensuring robust data ingestion, transformation, and storage.
- Implement ETL (Extract, Transform, Load) pipelines—preferably using Airflow—to guarantee clean, reliable data for AI model training and validation.
- Partner with data scientists, software engineers, and product teams to develop and deploy AI solutions that meet evolving business needs.
- Present progress and insights to both technical and non-technical stakeholders, driving consensus and facilitating decision-making.
- Optimize AI models and data pipelines to handle large data volumes and complex AI scenarios without compromising performance.
- Develop robust monitoring and alerting systems to maintain consistent model performance.
- Evaluate and fine-tune models for speed, accuracy, and cost-efficiency.
- Employ big data technologies (e.g., Apache Spark, Hadoop) and cloud platforms (Azure, Google Cloud, Databricks) to enhance operational efficiency.
- Maintain clear, comprehensive documentation for AI processes, data pipelines, and agentic workflows.
- Ensure adherence to industry standards, data governance, and compliance requirements.
Preferred Qualifications
- Exposure to insurance datasets and domain-specific challenges is a plus.