Machine Learning Engineer
Tasks
- Build and maintain real-time and batch data pipelines
- Deploy ML LLM solutions on cloud platforms
- Design train fine tune and deploy machine learning and LLM models
- Develop prompt engineering optimization and safety techniques
- Document processes experiments and results
- Experiment with emerging LLM SLM architectures and agent orchestration patterns
- Generate embeddings for structured and unstructured data
- Implement RAG pipelines with vector databases
- Implement model monitoring drift detection and retraining pipelines
- Integrate memory evidence packs and explainability modules into pipelines
- Mentor junior engineers and share ML engineering best practices
- Participate in design reviews architecture discussions and model evaluations
- Prototype and optimize multi agent workflows
- Translate research prototypes into production components
Perks/Benefits
- N/A
Skills/Tech-stack
AI Safety | AWS SageMaker | Agent systems | Anomaly Detection | Apache Spark | Autogen | Behavioral analytics | CI/CD | Databricks | Drift Detection | Embeddings | Explainable AI | Fraud Detection | Hugging Face | Hugging Face Transformers | Langchain | Langgraph | Language Models | Large Language Models | MCP | MLOps | Model Drift | Model Drift Detection | Model Monitoring | Multi-Agent | Multi-Agent Systems | Prompt engineering | PyTorch | Python | Ray | Retrieval-Augmented Generation | SQL | Scikit-learn | TensorFlow | Vector Databases
Education
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