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-based o...
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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