Director, AI Engineering
Tasks
- Build AI prototypes and validate technical approaches
- Collaborate with product on AI roadmaps and priorities
- Define AI governance guardrails and responsible AI practices
- Deploy monitor and improve production machine learning models
- Design LLM and model orchestration solutions
- Develop and operationalize machine learning pipelines
- Ensure AI reliability observability performance and cost efficiency
- Establish AI development lifecycle practices
- Evaluate and recommend LLMs and AI frameworks
- Guide engineers on generative AI and RAG best practices
- Implement MLOps practices for repeatable deployments
- Implement vector search and knowledge retrieval systems
- Lead AI platform design and architecture
- Mentor AI and machine learning engineers
- Partner with security and compliance for regulated AI deployments
- Review code and technical designs for scalability and security
Perks/Benefits
Skills/Tech-stack
AWS Lambda | AWS SageMaker | Agentic AI | Amazon Bedrock | Docker | Drift Detection | Experiment tracking | Feature Engineering | Feature Store | Kafka | Kubernetes | LLM Applications | Langchain | Langgraph | Language Models | Large Language Models | MCP | MLOps | MLflow | Machine Learning | Machine Learning Pipelines | Model Context Protocol | Model Deployment | Model Evaluation | Model Monitoring | Model Orchestration | Model Training | Model Validation | Model versioning | OpenSearch | PGVector | Prompt engineering | PyTorch | Python | Retrieval-Augmented Generation | Snowflake | TensorFlow | Vector Databases | Vector Search
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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