Required Qualifications
7 years in software engineering or applied ML building real-world AI/ML systems; strong Python proficiency and backend development expertise.
Hands-on experience building GenAI apps with LangChain and LangGraph including agent design state/memory management and graph-based orchestration.
Proficiency in ML/NLP and generative models; experience with embeddings vector stores RAG and LLM integration/fine-tuning (OpenAI LLaMA Cohere etc.)
Strong coding in Python and experience with frameworks/tools such as FastAPI PyTorch/TensorFlow MLflow; solid understanding of software engineering fundamentals and secure development.
Experience with AI agent frameworks and MCP; familiarity with agent observability (LangSmith/LangFuse) and agentic RAG patterns
Track record of delivering scalable production AI systems and collaborating across teams.
Experience with agent frameworks (AutoGen CrewAI) tool-use ecosystems and advanced planning/reasoning strategies
Knowledge of cloud platforms (AWS) MLOps and data pipelines; familiarity is a plus.
Exposure to enterprise environments and secure compliant deployments
Key Skills
- Programming: Python; backend APIs (FastAPI)
- AI/ML: ML/NLP generative AI embeddings model evaluation
- Frameworks: LangChain LangGraph; plus LlamaIndex PyTorch TensorFlow MLflow
- Architectures: RAG Transformers OCR
- Agents: Design and orchestration memory/state management tool integration; MCP and agent-to-agent protocols
- Observability: LangSmith/LangFuse for agent monitoring
Required Qualifications 7 years in software engineering or applied ML building real-world AI/ML systems; strong Python proficiency and backend development expertise. Hands-on experience building GenAI apps with LangChain and LangGraph including agent design state/memory management and graph-...
Required Qualifications
7 years in software engineering or applied ML building real-world AI/ML systems; strong Python proficiency and backend development expertise.
Hands-on experience building GenAI apps with LangChain and LangGraph including agent design state/memory management and graph-based orchestration.
Proficiency in ML/NLP and generative models; experience with embeddings vector stores RAG and LLM integration/fine-tuning (OpenAI LLaMA Cohere etc.)
Strong coding in Python and experience with frameworks/tools such as FastAPI PyTorch/TensorFlow MLflow; solid understanding of software engineering fundamentals and secure development.
Experience with AI agent frameworks and MCP; familiarity with agent observability (LangSmith/LangFuse) and agentic RAG patterns
Track record of delivering scalable production AI systems and collaborating across teams.
Experience with agent frameworks (AutoGen CrewAI) tool-use ecosystems and advanced planning/reasoning strategies
Knowledge of cloud platforms (AWS) MLOps and data pipelines; familiarity is a plus.
Exposure to enterprise environments and secure compliant deployments
Key Skills
- Programming: Python; backend APIs (FastAPI)
- AI/ML: ML/NLP generative AI embeddings model evaluation
- Frameworks: LangChain LangGraph; plus LlamaIndex PyTorch TensorFlow MLflow
- Architectures: RAG Transformers OCR
- Agents: Design and orchestration memory/state management tool integration; MCP and agent-to-agent protocols
- Observability: LangSmith/LangFuse for agent monitoring
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