Senior Machine Learning Engineer
Tasks
- Architect and manage scalable MLOps and LLMOps infrastructure
- Architect vector search solutions
- Build and scale MCP servers and integrate into AI ecosystems
- Build evaluation frameworks for agent performance
- Build high performance retrieval systems using vector databases
- Build recommendation systems using embeddings
- Create feedback loops for improving decision making
- Design CI CD pipelines for model training and deployment
- Design agentic systems for web extraction NER and entity resolution
- Establish metrics and experimentation frameworks
- Fine tune embedding models for semantic search
- Implement model monitoring for drift detection
- Own production workflows across data application API and MCP layers
- Translate business requirements into scalable ML solutions
Perks/Benefits
- N/A
Skills/Tech-stack
CI/CD | Distributed Systems | Drift Detection | Embeddings | Entity Resolution | Entity recognition | Experimentation | Faiss | Fault Tolerance | Graph Neural Networks | LLMOps | Language Processing | Latency monitoring | LoRA | MLOps | Machine Learning | Microservices | Model Drift | Model Drift Detection | Model Monitoring | Named Entity Recognition | Natural Language | Natural Language Processing | Neural Networks | Observability | PEFT | Pinecone | Prompt engineering | PyTorch | Python | RAG | REST APIs | Retrieval-Augmented Generation | Statistical validation | TensorFlow | Token Utilization Monitoring | Token utilization | Transformer | Two-Tower | Two-Tower architecture | Utilization Monitoring | Vector Clustering | Vector Databases | Vector Search
Education
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