ML Engineer (Applied AI)
Tasks
- Architect and build AI features
- Clean and process data for predictive analytics
- Conduct code reviews and validation testing
- Design retrieval pipelines and semantic search
- Evaluate AI quality reliability safety and impact
- Implement monitoring debugging and iterative improvement
- Integrate and deploy LLM applications
- Optimize prompts and chunking strategies
- Train and fine tune custom algorithms
- Translate business requirements into technical scope and prototypes
Perks/Benefits
- Hardware provided
- Maternity & paternity leave
- Paid Holidays
- Paid sick leave
- Paid vacation
- Professional development reimbursement
- Remote work
- Well-being budget
Skills/Tech-stack
AWS Bedrock | AWS EC2 | AWS Lambda | AWS SageMaker | Anomaly Detection | Classification | Data Engineering | Evaluation Methodologies | Hallucination detection | Langchain | Langgraph | Language Models | Language Processing | Large Language Models | Llamaindex | Machine Learning | Milvus | Natural Language | Natural Language Processing | PGVector | Pinecone | Prompt engineering | Python | Qdrant | Regression | Retrieval-Augmented Generation | SQL | Semantic Search | Serverless architecture | Transformer Models | Vector Databases
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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