Senior Software Engineer responsible for building and evolving external integration strategy, designing full‑stack features, and contributing to AI‑powered capabilities.
Responsibilities
Design and implement full‑stack features across frontend and backend systems
Develop React, TypeScript and Python components
Integrate AI capabilities such as LLMs and agentic workflows
Collaborate with product, design and backend teams
Own the agent layer of the agentic framework — the build, not the design. Run the agent development pipeline: LLM integration, prompt-to-action engineering, agent evaluation and testing, and the reusable agent components the rest of the function depends on.
Locations (3)
United Kingdom
India
United States
Responsibilities
Own the agent layer: agent development pipeline, LLM integration, prompt-to-action engineering and agent lifecycle.
Own agent evaluation and testing, including credible approaches to non-deterministic output.
Build GenAI security use cases such as alert triage automation and threat summarisation.
Develop reusable agent components that reduce effort on the next build.
Build to AI Architecture's specifications and raise design problems rather than engineering around them.
Personally build the most complex AI solutions Cyber Architecture and Solutions delivers using LangChain, LlamaIndex, vector databases and production Python
Build the shared SDK, evaluation harness and reusable components the agent and platform workstreams both depend on
Lead the hardest engineering problems in the agentic framework build
Develop reusable engineering components, libraries and patterns the wider team builds on
Build to the architecture specification and raise design problems early rather than engineering around them
Requirements
Fullstack Data Engineer - Applied & Agentic AI Systems
Lead the design, implementation, and operation of data infrastructure for agentic, LLM‑based AI solutions within Roche Digital Technology’s Applied AI Engineering Team.
Responsibilities
Collaborate with AI engineers, data scientists, product owners, and developers to integrate LLMs into scalable, robust, fair, and ethical end‑user applications.
Design and implement scalable, high‑performance data pipelines for AI/GenAI applications, ensuring efficient ingestion, transformation, storage and retrieval.
Work with vector databases such as AWS OpenSearch and Azure AI Search to enable similarity search and RAG for generative AI.
Build and maintain cloud‑based data solutions using AWS (OpenSearch, S3, Lambda) or Azure (AI Search, Blob Storage).
Design and optimize data storage and processing using Snowflake for scalable, cloud‑native analytics.
Requirements
Bachelor's degree in Computer Science, Data Engineering or related fields
Design, build and operate the core platform powering JazzX AI’s enterprise digital workers, including agent runtime, workflow orchestration, and integration capabilities.
Responsibilities
Design and build production‑grade backend services and reusable platform capabilities for agent runtime, orchestration, state, memory, guardrails and model access.
Develop agentic workflows using planning, tool use, multi‑agent collaboration, parallel execution, reflection and human‑in‑the‑loop.
Build durable, long‑running workflows with retries, timeouts, state management, recovery and compensation.
Design asynchronous and event‑driven systems using queues, messaging and distributed processing.
Build secure enterprise integrations, APIs and protocol‑based interfaces for tool invocation and agent‑to‑agent communication.
A Head of Engineering to lead and scale our engineering organization, drive technical strategy, and take end-to-end ownership of engineering execution and production operations.
Responsibilities
Build, lead, and scale a high-performing engineering organization, including hiring, mentoring, and developing engineering leaders and teams.
Own end-to-end engineering execution, ensuring predictable delivery, technical excellence, and alignment with business priorities.
Take full ownership of production environments and operations, including DevOps, infrastructure, deployment, monitoring, observability, availability, performance, incident management, disaster recovery, and operational reliability.
Lead architectural and technical decisions, ensuring the platform is scalable, secure, reliable, and maintainable.
Define and drive engineering strategy, standards, best practices, and long-term technical direction.
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