Manager - Senior AI/ML Engineer
Tasks
- Architect MCP clients and servers
- Build agentic systems
- Build production services with Python
- Define evaluation strategies and quality gates
- Design RAG platforms
- Establish observability for AI workflows
- Implement CI/CD and cloud deployment
- Integrate agents with enterprise systems
- Lead architecture reviews and mentor engineers
- Own AI and ML solutions end to end
Perks/Benefits
Skills/Tech-stack
API Design | AWS | Agent Orchestration | Agent systems | Asynchronous Workflows | Azure | CI/CD | Cost Optimization | Deep learning | Distributed Systems | Docker | Embeddings | Evaluation | Event Driven | Event-driven architecture | Experiment tracking | Function Calling | Generative AI | Git | Human-in-the-loop | Hybrid search | Infrastructure as Code | Injection Protection | Kubernetes | LLM | LLMOps | MLOps | Machine Learning | Model Context Protocol | Model versioning | Monitoring | Multi-Agent | Multi-Agent Systems | NumPy | Pandas | Prompt engineering | Prompt injection | Prompt versioning | Prompt-injection protection | PyTorch | Python | Reranking | Retrieval-Augmented Generation | Scikit-learn | Secure Authentication | Structured outputs | TensorFlow | The Loop | Tool-Calling | Tracing | Vector Search | “as-code”
Education
Regions
Countries
States
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