Machine Learning Systems Engineer
Tasks
- Architect scalable backend services
- Benchmark models and safely roll out upgrades
- Build media ingestion processing pipelines
- Build reliable asynchronous processing patterns
- Build semantic search and retrieval workflows
- Deploy and operate systems on cloud infrastructure
- Design data models for media assets metadata and embeddings
- Ensure reliability using observability and incident response
- Implement event driven workflows using queues and pub sub
- Implement vector search and indexing systems
- Integrate AI ML model workflows into production
- Mentor engineers and lead technical planning
- Monitor performance and cost per processed item
- Optimize model inference latency throughput reliability and cost
- Serve models using inference servers
Perks/Benefits
Skills/Tech-stack
AWS | Alerting | Amazon SageMaker | Asynchronous processing | Autoscaling | Azure | Backpressure | Batching | Caching | Cloud Computing | Data Modeling | Database Design | Dead Letter | Dead Letter Queue | Distributed Systems | Elasticsearch | Embedding Generation | Event Driven | Event-driven architecture | Faiss | GCP | GPU Utilization | Hugging Face | Hugging Face Transformers | Idempotency | Incident Response | JavaScript | Knowledge Distillation | Logging | Metrics | Microservices | Model Serving | Monitoring | Multimodal Learning | NVIDIA Triton | NoSQL | OCR | Observability | PGVector | Pinecone | Prompt Optimization | PyTorch | Python | Qdrant | Quantization | REST APIs | Retries | SQL | SQL schema | SQL schema design | Scene detection | Schema Design | Semantic Search | Similarity Search | Speech-to-Text | System design | TensorFlow | Text Generation | Text generation inference | Tracing | Transformers | VLLM | Vector Search | Vertex AI | Weaviate
Education
N/A
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