Senior Python AI Engineer (LLM & Multi-Agent Systems)
Undisclosed company
SeniorHybridFull-timeAI & ML
Confirmed open at the employer less than an hour ago · Posted 21 hours ago
Requirements
pythonllmfastapilangchainreact
Nice to have
elasticsearchdata structures
Job description
We are looking for a Senior Backend Engineer specialized in Generative AI to design agent workflows, optimize interactions with models (OpenAI, AWS Bedrock), and ensure the reliability of non-deterministic systems in production.
Tech Stack: Python (Asyncio), FastAPI, LangChain, LangGraph, Pydantic, Elasticsearch, AWS Bedrock / OpenAI API, LangSmith.
What You'll Do:
Agent Architecture: Design and implement complex agent orchestration logic using LangGraph. You will define state management, conditional routing, and error handling within the agent graph.
Tool Engineering: Build and optimize the tool layer (function calling) that allows LLMs to interact with internal financial APIs and databases accurately.
Performance Optimization:
-Reduce end-to-end latency through asynchronous processing and streaming (SSE).
-Implement semantic caching strategies to minimize API costs and response time.
-Optimize token usage without sacrificing answer quality.
Observability & Evaluation: Implement automated evaluation pipelines using LangSmith. You will be responsible for setting up regression testing for prompts and agents to measure quality (correctness, faithfulness) before deployment.
Advanced RAG: Refine retrieval strategies. Work on hybrid search implementation (Keyword + Vector), re-ranking, and query expansion to feed the most relevant context to the model.
Tech Stack: Python (Asyncio), FastAPI, LangChain, LangGraph, Pydantic, Elasticsearch, AWS Bedrock / OpenAI API, LangSmith.
What You'll Do:
Agent Architecture: Design and implement complex agent orchestration logic using LangGraph. You will define state management, conditional routing, and error handling within the agent graph.
Tool Engineering: Build and optimize the tool layer (function calling) that allows LLMs to interact with internal financial APIs and databases accurately.
Performance Optimization:
-Reduce end-to-end latency through asynchronous processing and streaming (SSE).
-Implement semantic caching strategies to minimize API costs and response time.
-Optimize token usage without sacrificing answer quality.
Observability & Evaluation: Implement automated evaluation pipelines using LangSmith. You will be responsible for setting up regression testing for prompts and agents to measure quality (correctness, faithfulness) before deployment.
Advanced RAG: Refine retrieval strategies. Work on hybrid search implementation (Keyword + Vector), re-ranking, and query expansion to feed the most relevant context to the model.
Requirements:
Requirements:
Python Expert: Strong proficiency in modern Python. Deep understanding of asynchronous programming (asyncio) patterns is mandatory, as our entire I/O pipeline (Network, DB, LLM) is non-blocking. Experience with FastAPI and Pydantic (v2).
Agentic Frameworks: Production experience with LangChain. Hands-on experience or deep conceptual understanding of LangGraph (or similar state-machine based agent frameworks).
Deep LLM Expertise (What we mean by "Deep"):
Non-determinism Management: Strategies for handling LLM hallucinations and ensuring reliable outputs (e.g., self-correction loops, specific prompting techniques like CoT/ReAct).
Structured Outputs: Experience forcing LLMs to adhere to strict schemas (Pydantic/JSON mode) for reliable downstream processing.
Context Optimization: Advanced strategies for managing limited context windows (summarization chains, sliding windows, selective context injection) beyond simple truncation.
Inference Economics: Understanding the trade-offs between model size, latency, and cost (e.g., when to route to GPT-4 vs. a smaller/faster model).
Nice to Have:
Experience with Elasticsearch (DSL queries, analyzers).
Knowledge of vector databases and embedding models.
Background in FinTech or familiarity with financial data structures.
Requirements:
Python Expert: Strong proficiency in modern Python. Deep understanding of asynchronous programming (asyncio) patterns is mandatory, as our entire I/O pipeline (Network, DB, LLM) is non-blocking. Experience with FastAPI and Pydantic (v2).
Agentic Frameworks: Production experience with LangChain. Hands-on experience or deep conceptual understanding of LangGraph (or similar state-machine based agent frameworks).
Deep LLM Expertise (What we mean by "Deep"):
Non-determinism Management: Strategies for handling LLM hallucinations and ensuring reliable outputs (e.g., self-correction loops, specific prompting techniques like CoT/ReAct).
Structured Outputs: Experience forcing LLMs to adhere to strict schemas (Pydantic/JSON mode) for reliable downstream processing.
Context Optimization: Advanced strategies for managing limited context windows (summarization chains, sliding windows, selective context injection) beyond simple truncation.
Inference Economics: Understanding the trade-offs between model size, latency, and cost (e.g., when to route to GPT-4 vs. a smaller/faster model).
Nice to Have:
Experience with Elasticsearch (DSL queries, analyzers).
Knowledge of vector databases and embedding models.
Background in FinTech or familiarity with financial data structures.
This position is open to all candidates.
Listing published by its original source and linked back to it. JobsWarm does not receive payment from employers.