Lead AI Engineer
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Key skills for this role
Role Overview
Lead innovative AI and machine learning initiatives and guide the development and rollout of generative AI applications.
Drive AI-powered efficiencies and business results while working with teams across the organization.
Key Skills for This Role
Full Job Posting
Role overview
Lead innovative AI and machine learning initiatives and guide the development and rollout of generative AI applications.
Drive AI-powered efficiencies and business results while working with teams across the organization.
Responsibilities
- Construct and launch scalable AI and machine learning models and generative AI solutions in production environments.
- Build and maintain dependable ETL pipelines and data workflow processes.
- Create applications powered by large language models using contemporary development frameworks.
- Apply prompt engineering strategies to improve the quality of AI-generated outputs.
- Deploy models through APIs and integrate them with enterprise applications.
- Own development, deployment, monitoring, and refinement across the solution lifecycle.
- Collaborate with cross-functional teams to deliver AI solutions aligned with business goals.
Requirements
- 8 to 13 years of broad IT experience.
- More than 8 years of AI engineering experience.
- At least 1 year of experience in a leadership role.
- Solid programming ability in Python.
- Direct experience building ETL processes and data pipelines.
- Strong background in machine learning and deep learning frameworks, such as TensorFlow, PyTorch, and Scikit-learn.
- Working knowledge of prompt engineering and hands-on LLM experience.
- Familiarity with LangChain, LlamaIndex, and Hugging Face.
- Practical experience with Azure, AWS, or GCP.
- Experience using MLflow, Kubeflow, or Airflow.
- Background in API development and deploying models into production.
- English proficiency at B2 level or higher, with strong technical communication.
Nice to have
- Background working with retrieval-augmented generation architectures and vector databases.
- Familiarity with real-time or streaming data pipeline design.
- Exposure to scalable systems and microservices-based architecture.
- Experience with Databricks and PySpark.
- Experience with cloud-based data solutions and ETL pipeline development.
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