דרושים » AI » AI Solutions Delivery עבור חברת הייטק באזור השרון

We are looking for a Backend Prompt Engineer with 4+ years of backend experience and proven hands on delivery of GenAI powered features in production environments.

This is not a research or experimentation role. Prompt engineering is system design. You will design reliable, scalable, production ready LLM workflows that power real customer facing capabilities inside a complex distributed platform.

You will work closely with backend engineers, product managers, and architects to integrate LLM based intelligence into core business flows.

Location: Kfar Saba, Israel

Reporting to: AI Team Lead

Roles and Responsibilities:
Design, implement, and continuously improve prompts for LLM driven product features.
Architect and develop backend services in Python.
Integrate LLM APIs such as OpenAI, Anthropic, and AWS Bedrock into production systems.
Implement structured output enforcement, schema validation, and response normalization.
Design robust error handling, fallback strategies, retries, and resiliency mechanisms.
Optimize latency, token usage, throughput, and API cost efficiency.
Build evaluation frameworks and quality control pipelines for AI outputs.
Collaborate with Product and Engineering teams to deliver AI features end to end within us.

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.