Senior Machine Learning Engineer
Tasks
- Apply prompt engineering and guardrails
- Architect production AI systems
- Build REST and streaming APIs
- Build agentic systems and orchestration
- Build and deploy ML and GenAI workloads
- Build solutions on Databricks and AWS
- Conduct code reviews and mentor engineers
- Design scalable inference pipelines
- Develop LLM evaluation traces and replay tooling
- Drive agentic SDLC for design test evaluate deploy monitor
- Embed data lineage access controls audit trails
- Implement RAG and fine-tuning
- Implement drift detection and evaluation harnesses
- Implement humaninloop patterns
- Implement toolfunction calling integrations
- Integrate Redis and DynamoDB and ElastiCache
- Integrate vector stores and graph databases
- Monitor and manage ML lifecycle
- Perform model training serving and governance
- Provision infrastructure using infrastructure-as-code
Perks/Benefits
- Annual grants for nonprofit organizations
- Charitable matching gifts
- Flexible work options
- Generous time away
- Health benefits
- Mentorship
- Professional development resources
Skills/Tech-stack
AWS | Amazon DynamoDB | Amazon ElastiCache | Apache Spark | CI/CD | Containers | Data Governance | Databricks | DevOps | Drift Detection | Fine Tuning | Generative AI | Graph Databases | Guardrails | Human-in-the-loop | Inference pipeline | Infrastructure as Code | Language Models | Large Language Models | MLflow | Machine Learning | Model Evaluation | Model Monitoring | Observability | Prompt engineering | Python | REST API | Redis | Retrieval-Augmented Generation | Streaming APIs | The Loop | Unity Catalog | Vector Databases | “as-code”
Education
Bachelor of Engineering | Bachelor of Science | Bachelor's Degree
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