AI/ML Engineer – Intelligent Systems & AI Infrastructure
Tasks
- Analyze failure modes
- Architect scalable backend services
- Automate operational workflows
- Build LLM powered document analysis
- Build REST API integrations
- Build conversational voice agents
- Build real time and batch processing
- Create reproducibility workflows
- Design AI agents
- Design data ingestion pipelines
- Design evaluation frameworks and metrics
- Design knowledge retrieval systems
- Design multi-agent architectures
- Develop CI/CD pipelines
- Develop machine learning systems
- Develop modular AI pipelines
- Enforce privacy and compliance
- Ensure secure data handling
- Evaluate AI outputs and conversations
- Implement model versioning
- Implement monitoring logging and observability
- Implement retrieval-augmented generation pipelines
- Implement safeguards against misuse
- Implement vector search infrastructure
- Integrate internal and external data sources
- Manage API credentials access control and authentication
- Manage embedding pipelines
- Monitor model drift and performance
- Optimize inference pipelines
- Process clean and transform datasets
- Set up experiment tracking
- Test conversational agents and workflows
- Update embeddings knowledge bases and feature stores
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | Azure | CI/CD | Dialogue Orchestration | Docker | Experiment tracking | Faiss | Feature Store | GCP | HuggingFace Transformers | Kubeflow | Kubernetes | Langchain | Language Models | Language Processing | Large Language Models | ML model monitoring | MLOps | MLflow | Machine Learning | Model Monitoring | Model versioning | Natural Language | Natural Language Processing | Observability | Pinecone | Prompt engineering | PyTorch | Python | REST API | Retrieval-Augmented Generation | Speech Recognition | Vector Database | Vector Search | Weaviate | Weights and Biases
Education
Roles
AI | AI Engineer | Engineer | Learning Engineer | MLOps Engineer | Machine Learning Engineer
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