Senior Ai/Ml Engineer
Tasks
- Address prompt injection defenses
- Apply responsible AI and guardrails
- Build agentic systems with LLM orchestration
- Build and deploy ML models end to end
- Create evaluation frameworks and automated eval suites
- Deliver MLOps and LLMOps pipelines
- Design and deliver GenAI solutions with RAG
- Design and deliver machine learning solutions in production
- Detect model drift
- Ensure regulation compliant AI with data residency
- Implement Retrieval-Augmented Generation architectures
- Implement output guardrails
- Implement production monitoring for AI systems
- Manage model registry and versioning
- Run A B testing for model improvements
- Set up observability for models and prompts
Perks/Benefits
- Additional dental care
- Childcare for extra flexibility
- Childcare support
- Company stock plan
- Dental care
- Gear stipend
- Healthcare coverage
- Home office budget
- IT & remote budget
- Learning and development
- Meal benefit
- Mental health support
- Monthly massage
- One off home cleaning service
- Phone benefit
- Public transportation benefit
- Remote work
- Sports and culture benefit
- Travel insurance
- Weekly wellbeing hour
- Yearly bonus
Skills/Tech-stack
A/B | A/B Testing | AWS | Agentic Systems | Azure | Azure ML | B testing | Container Orchestration | DBT | Data Residency | Databricks | Databricks MLFlow | Deep learning | Drift Detection | Embeddings | Evaluation | Feature Engineering | Generative AI | Google Cloud | Identity integration | Infrastructure as Code | Injection Defense | LLM orchestration | LLMOps | MLOps | Machine Learning | Model Deployment | Model Drift | Model Drift Detection | Model Registry | Multi-step Planning | Observability | Output guardrails | Prompt engineering | Prompt injection | Prompt injection defense | RAG | Reranking | Retrieval-Augmented Generation | SageMaker | Snowflake | Training pipelines | Vertex AI | “as-code”
Education
N/A
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