Machine Learning Systems Engineer
Tasks
- Architect backend services for media intelligence platform
- Benchmark and evaluate models for quality and latency
- Build and operate scalable media ingestion pipelines
- Build semantic search retrieval workflows
- Deploy systems and manage cloud infrastructure
- Design asynchronous job execution patterns
- Design data models for metadata embeddings and search indexes
- Ensure reliability with logging metrics tracing alerting
- Implement event driven media processing workflows
- Implement vector search and indexing systems
- Integrate AI ML inference into backend workflows
- Mentor engineers and guide technical planning
- Monitor production performance and costs
- Optimize model inference for latency throughput reliability cost
- Serve and manage AI models for real time and batch
Perks/Benefits
Skills/Tech-stack
AWS | Alerting | Amazon SQS | Amazon SageMaker | Apache Kafka | Asynchronous processing | Autoscaling | Backpressure | Batching | Caching | Cloud Pub/Sub | Data Modeling | Database Design | Dead Letter | Dead Letter Queue | Distributed Systems | Elastic Vector Search | Embeddings | Event Driven | Event-driven architecture | Faiss | Fault-tolerant | GPU Utilization | Google Cloud | Google Cloud Pub/Sub | Hugging Face | Hugging Face Transformers | Idempotency | Incident Response | Indexing | Inference Server | Logging | Metrics | Microservices | Microsoft Azure | Model Distillation | Monitoring | Multimodal AI | NoSQL | Node.js | OCR | PGVector | Pinecone | Prompt Optimization | Pub/Sub | PyTorch | Python | Qdrant | Quantization | Queues | REST API | Retries | Retrieval-Augmented Generation | SQL | Scene detection | Semantic Search | Similarity Search | Speech-to-Text | System design | TensorFlow | Text Generation | Text generation inference | Tracing | Transcription | Triton Inference | Triton Inference Server | VLLM | Vector Databases | Vector Search | Vertex AI | Weaviate
Education
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