You will bridge the gap between functional design and technical implementation by creating low-level designs and prototype code for agentic AI systems. This involves running independent proofs of concept, proving integration patterns, and documenting solutions for a distributed development team.
This is a remote position.
A global consumer goods brand runs a customer-facing AI agent - agentic RAG over its product range - that answers customer questions in natural language. It is already live in couple European markets and is onboarding roughly one new country every month, each with its own language and regulatory nuances. The programme is sponsored at board level and carries high internal visibility.This role exists to close the gap between functional design and buildable design. You take the functional (non-technical) design, resolve as many of the unknowns as you can, and produce a low-level design the development team can build from without rediscovering the same problems. In practice that means writing real prototype code, proving integration patterns end to end, and documenting what works clearly enough for a distributed team to implement it.
WHAT YOU'LL BE DOING
Turning functional designs into low-level technical designs that development teams can build from directly
Running spikes and proofs of concept independently - scoping them, building them and reaching a clear conclusion
Proving integration patterns to surrounding systems end to end: authentication, authorisation, secrets management and API contracts
Designing and prototyping agent behaviour with LangChain and LangGraph; instrumenting, tracing and evaluating with LangSmith
Implementing MCP integrations and agent-to-agent communication patterns
Writing documentation and reference code clear enough for a distributed team to follow without you in the room
Working alongside the tech lead and architects so that prototype decisions stay consistent with the target architecture
Driving open questions to resolution across teams - raising the issue, convening the right people and closing the loop
Requirements
Hands-on experience with LangChain, LangGraph and LangSmith — building, debugging and evaluating agent systems, not just familiarity
Strong, current Python development skills
Experience building interfaces to other systems, including authentication, authorisation and secrets management
Practical experience implementing Model Context Protocol (MCP) integrations
Experience with agent-to-agent (A2A) integration patterns
Able to conduct proofs of concept independently, from scoping through to a documented conclusion
Comfortable working closely with both a development team and solution architects
Proactive communicator — able to set up meetings, chase answers across teams and resolve blockers without being asked
Clear technical writing and example code that others can work from
NICE TO HAVE
Experience with telephony and call centre applications - the platform uses Twilio
Experience with multilingual or multi-market AI deployments
RAG evaluation and answer-quality measurement at production scale
Experience working with distributed teams across time zones
Exposure to regulated or compliance-sensitive content domains
WHAT SUCCESS LOOKS LIKE
Development teams start work with fewer open questions and fewer false starts
Integration patterns are proven once and reused across markets, rather than rediscovered each time
Low-level designs are specific enough to build from - and are actually used by the teams they were written for
Technical unknowns surface and get resolved before they become delivery blockers
TECH ENVIRONMENT
Core stack
Python
LangChain, LangGraph, LangSmith
Agentic RAG over product content; multi-language and multi-market
MCP and agent-to-agent integration
Authentication, authorisation and secrets management across system boundaries
Twilio for telephony and call centre channels
Working context
Product owner, management and tech lead based in Poland
English as the working language across the delivery team
“I was the first applicant for a remote marketing position that got listed on the company website the same day I applied. Had an interview within 48 hours!”
— Sarah J.
·
Marketing Manager
★★★★★
Verified
