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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Engineer, Agentic Systems - **Company:** jazzhr - **Location:** United States (Remote available) - **Experience:** Expert - **Contract:** Temporary contract - **Skills:** Artificial Intelligence, Amazon Web Services, Python (Programming Language), PostgreSQL, Routing, Recommender Systems, Redis, Data Streaming, Large Language Models, Multi-Agent Systems, Caching, Git, Fastapi, Low Latency, Asynchronous Programming, Api Design, Docker - **Published:** September 18, 2026 - **Apply:** http://talentwwinc.applytojob.com/apply/jobs/details/QDqPA7Lqko ## About the Role * 5+ years as a software or ML engineer, including at least 2 years shipping LLM-based products to real users * Experience building a conversational product that people used, and a clear account of what worked and what didn't * Production experience with agent frameworks (LangGraph or similar), tool calling, MCP, structured outputs, state management * Experience evaluating multi-turn systems: offline eval sets, judge validation, online testing * Experience with guardrails and adversarial input in a live product * Experience with streaming, latency and cost optimisation for LLM systems * Python, asynchronous programming, and API design; Git, Docker, and AWS. * Interest in the problem domain: careers, coaching, how people make decisions about work THE NICE-TO-HAVES * Fine-tuning or post-training on conversational data (SFT, DPO, distillation) * Voice interfaces: STT, TTS, real-time conversational agents * Consumer product experience * Background in coaching, education, health or similar * MCP server development * Classical ML or recommendation systems ## Description We are developing a new product, an AI career companion app. It is distinct from our main platform and is in its early stages. At its heart is a conversational assistant which helps people with their job search, along with an agentic feature that enables it to carry out actions on their behalf such as updating a resume, searching for jobs, preparing for an interview, and following up on the outcome. You would have control of that core since the team is small and the direction has already been decided, most of the technical choices being yours., Conversation and Memory * A multi-turn assistant that maintains context across sessions over weeks or months * User memory and personalisation: what someone has shared, what they've done so far, what's changed * Retrieval over the coaching corpus, and fine-tuning if it turns out to be worth it * Conversation design: when the assistant should ask, suggest, act, or refer the user elsewhere Agent Orchestration * Multi-step workflows with explicit state, tool calling and handoffs between specialized agents * Deciding which actions need user confirmation and which don't * Routing between model providers based on cost, latency and quality, with fallbacks * Traces for every agent decision so failures can be debugged Evaluation * Offline evaluation sets for multi-turn conversations * LLM-as-judge scoring, validated against human ratings * Online experiments on live traffic, measured against user outcomes (applications, interviews, offers) Safety * Guardrails for scope, tone and escalation in a consumer product * Prompt injection and jailbreak handling * GDPR and EU AI Act compliance as part of the design Production * Streaming responses and latency work * Cost per conversation: caching, routing, prompt compression * Deployment: FastAPI, async Python, Postgres, Redis, AWS ## Related Videos - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Reducing LLM Calls with Vector Search Patterns - Raphael De Lio (Redis)](https://www.wearedevelopers.com/videos/1714-reducing-llm-calls-with-vector-search-patterns-raphael-de-lio-redis) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Beyond Chatbots: How to build Agentic AI systems](https://www.wearedevelopers.com/videos/1629-beyond-chatbots-how-to-build-agentic-ai-systems) - [Git for Code Reviews](https://www.wearedevelopers.com/videos/429-git-for-code-reviews) - [When Should You Use an Agent? Architectural Trade-offs in Agentic Systems](https://www.wearedevelopers.com/videos/100109-when-should-you-use-an-agent-architectural-trade-offs-in-agentic-systems) ## Related Articles - [What is Agentic Programming and Why Should Developers Care?](https://www.wearedevelopers.com/magazine/625-what-is-agentic-programming-and-why-should-developers-care) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path) - [Dev Digest 137 - AI'm not sure about this](https://www.wearedevelopers.com/magazine/485-dev-digest-137-ai-m-not-sure-about-this)