ኢዮብ
ከአጋር ምግብ
AI Software Engineer
ዋጋ በጥያቄ
ዝርዝሮች
- የቅጥር አይነት
- የሙሉ ጊዜ
- የርቀት
- አይ
- ኩባንያ
- Extrac.ai
መግለጫ
About ExTrac
ExTrac is a decision intelligence company used by governments, defence organisations, financial institutions, and corporates operating in complex, fast-moving environments. Our capabilities fuse curated data sources, domain-specific AI, and deep human expertise to transform information overload into clear, actionable foresight.
Our ambition is to become the analytical backbone that organisations rely on when geopolitical uncertainty becomes an opportunity or a strategic risk. More at extrac.ai .
The Role
We are looking for a Software Engineer to join ExTrac's AI team, building Co-Analyst and the analytical AI features around it.
Co-Analyst is a user-facing multi-agent system that works alongside intelligence analysts to research and write reports. A planning loop decomposes an analyst's question, fans work out to sub-agents, and assembles the results into a report where every claim traces back to the chunk of source it came from. Underneath sits hybrid retrieval over a large unstructured corpus: analyst intent translated into structured filters, combined with keyword and dense vector search, across multiple languages and media types.
The agent work is the centrepiece but not the whole job. In a single quarter the work spans agent orchestration, retrieval, graph analytics, and long-running streaming pipelines, alongside the services and databases underneath them. You will own well-defined features and components end to end across a Python and Go codebase, working alongside senior engineers who set technical direction, the data team who own the ingestion pipelines, and a research-focused ML team who train and evaluate the models we integrate and serve. The loop is short: product brings an idea, often recent and unproven, and our job is to spike an implementation and take it to a production feature. New features land close to weekly.
This hire exists to add capacity on the AI team's hard problems: an engineer who can independently deliver well-scoped features and components, and who is building towards owning more of the system end to end.
What the job involves
Agentic and analytical AI features
• Build and improve components of the agent loop itself: context assembly, tool selection, and sub-agent orchestration, with guidance from senior engineers on the team.
• Build the analytical AI features that sit alongside it, from network construction through to the summaries analysts read.
• Help prove that changes are improvements, running experiments against live analyst traffic behind feature flags.
• Contribute to agreeing what "better" means for a capability, and flag honestly when the evidence says a promising approach is not working.
• Work with embeddings as more than a retrieval concern. The same vectors drive network construction and community detection.
• Work within a model-agnostic design, swapping models and embeddings on the back of the ML team's evaluations rather than being locked to one.
Service design and delivery
• Take a well-defined problem, clarify requirements with your lead or a senior engineer, and ship it to production with regular check-ins rather than close oversight.
• Own features and components end to end within a single system, contributing to system design and architecture discussions and taking on more of the design work as you build context.
• Build and maintain APIs used by internal teams and customers, following established contracts and versioning conventions.
• Work with the storage layer as a design concern rather than an implementation detail: schema, indexing strategy, and access patterns, across relational, document, and vector stores.
Production engineering
• Build and operate supporting services across Python and Go, with growing ownership of design, deployment, and operations as you build track record.
• Help hold agent workflows to production standards for latency, cost, and reliability, in a system where non-determinism is a given.
• Build and operate long-running streaming pipelines, including the caching and recovery behaviour that makes them survivable.
Working with analysts, product, and the ML team
• Work directly with the analysts who use our products, turning what they hit in practice into changes in the system.
• Iterate quickly against a live stream of product requests, flagging where they collide with longer-horizon capability work.
• Partner with the ML team on agentic approaches, helping take proven concepts to production and working through the engineering, performance, and reliability problems a research implementation does not have to.
• Help integrate and serve the models they train, and contribute to the production infrastructure their evaluation frameworks run on.
You should apply if
• You have built and worked on production backend systems, and you want to keep doing that. You will spend more of your time in Python and Go services, databases, and APIs than in a prompt file, and you know the difference between something that demos well and something that holds up under production load, latency, and cost.
• You can work independently on well-defined problems, and know when to flag ambiguity or ask for input rather than guessing. When the obvious approach fails, you look for another one before escalating.
• You are curious about agentic frameworks and comfortable working without one. Experience with them is useful, but we build most of our own orchestration, because off-the-shelf abstractions have not survived our requirements around evaluation, control, and production performance.
• You can take a recent technique or paper, help spike an implementation, and reason clearly about whether it is worth taking further.
• You do not trust a change until you have measured it. Reaching for the evaluation is instinct rather than afterthought.…
Source: Arbeitnow (https://www.arbeitnow.co.uk/jobs/companies/extracai/ai-software-engineer-london-230361)
ExTrac is a decision intelligence company used by governments, defence organisations, financial institutions, and corporates operating in complex, fast-moving environments. Our capabilities fuse curated data sources, domain-specific AI, and deep human expertise to transform information overload into clear, actionable foresight.
Our ambition is to become the analytical backbone that organisations rely on when geopolitical uncertainty becomes an opportunity or a strategic risk. More at extrac.ai .
The Role
We are looking for a Software Engineer to join ExTrac's AI team, building Co-Analyst and the analytical AI features around it.
Co-Analyst is a user-facing multi-agent system that works alongside intelligence analysts to research and write reports. A planning loop decomposes an analyst's question, fans work out to sub-agents, and assembles the results into a report where every claim traces back to the chunk of source it came from. Underneath sits hybrid retrieval over a large unstructured corpus: analyst intent translated into structured filters, combined with keyword and dense vector search, across multiple languages and media types.
The agent work is the centrepiece but not the whole job. In a single quarter the work spans agent orchestration, retrieval, graph analytics, and long-running streaming pipelines, alongside the services and databases underneath them. You will own well-defined features and components end to end across a Python and Go codebase, working alongside senior engineers who set technical direction, the data team who own the ingestion pipelines, and a research-focused ML team who train and evaluate the models we integrate and serve. The loop is short: product brings an idea, often recent and unproven, and our job is to spike an implementation and take it to a production feature. New features land close to weekly.
This hire exists to add capacity on the AI team's hard problems: an engineer who can independently deliver well-scoped features and components, and who is building towards owning more of the system end to end.
What the job involves
Agentic and analytical AI features
• Build and improve components of the agent loop itself: context assembly, tool selection, and sub-agent orchestration, with guidance from senior engineers on the team.
• Build the analytical AI features that sit alongside it, from network construction through to the summaries analysts read.
• Help prove that changes are improvements, running experiments against live analyst traffic behind feature flags.
• Contribute to agreeing what "better" means for a capability, and flag honestly when the evidence says a promising approach is not working.
• Work with embeddings as more than a retrieval concern. The same vectors drive network construction and community detection.
• Work within a model-agnostic design, swapping models and embeddings on the back of the ML team's evaluations rather than being locked to one.
Service design and delivery
• Take a well-defined problem, clarify requirements with your lead or a senior engineer, and ship it to production with regular check-ins rather than close oversight.
• Own features and components end to end within a single system, contributing to system design and architecture discussions and taking on more of the design work as you build context.
• Build and maintain APIs used by internal teams and customers, following established contracts and versioning conventions.
• Work with the storage layer as a design concern rather than an implementation detail: schema, indexing strategy, and access patterns, across relational, document, and vector stores.
Production engineering
• Build and operate supporting services across Python and Go, with growing ownership of design, deployment, and operations as you build track record.
• Help hold agent workflows to production standards for latency, cost, and reliability, in a system where non-determinism is a given.
• Build and operate long-running streaming pipelines, including the caching and recovery behaviour that makes them survivable.
Working with analysts, product, and the ML team
• Work directly with the analysts who use our products, turning what they hit in practice into changes in the system.
• Iterate quickly against a live stream of product requests, flagging where they collide with longer-horizon capability work.
• Partner with the ML team on agentic approaches, helping take proven concepts to production and working through the engineering, performance, and reliability problems a research implementation does not have to.
• Help integrate and serve the models they train, and contribute to the production infrastructure their evaluation frameworks run on.
You should apply if
• You have built and worked on production backend systems, and you want to keep doing that. You will spend more of your time in Python and Go services, databases, and APIs than in a prompt file, and you know the difference between something that demos well and something that holds up under production load, latency, and cost.
• You can work independently on well-defined problems, and know when to flag ambiguity or ask for input rather than guessing. When the obvious approach fails, you look for another one before escalating.
• You are curious about agentic frameworks and comfortable working without one. Experience with them is useful, but we build most of our own orchestration, because off-the-shelf abstractions have not survived our requirements around evaluation, control, and production performance.
• You can take a recent technique or paper, help spike an implementation, and reason clearly about whether it is worth taking further.
• You do not trust a change until you have measured it. Reaching for the evaluation is instinct rather than afterthought.…
Source: Arbeitnow (https://www.arbeitnow.co.uk/jobs/companies/extracai/ai-software-engineer-london-230361)
ይህ ዝርዝር ከአጋር ምግብ የመጣ ነው። ከምንጩ ድህረ ገጽ ላይ ያመልክቱ።
ምንጭ: Extrac.ai
አካባቢ
London, ዩናይትድ ኪንግደም
ዝርዝር የቀረበው በExtrac.ai ነው።
