Senior AI Engineer

MMC

Porto

Hybrid

EUR 90,000 - 120,000

Full time

14 days+

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Benefits offered by this job

Competitive salary and equity package

Porto office with hybrid schedule (min

Top-of-the-line equipment

Private health insurance

Sick and compassionate leave

Job summary

Condukt is seeking a Senior AI Engineer to own data pipelines, context management, and multi-agent systems that power our compliance platform. You will ship production-grade AI features, ensure data quality, security, and observability, and collaborate with ML and backend engineers to scale from prototype to reliable services.

Based in Porto, you’ll join an ambitious team, work across the stack, and help define patterns, tooling, and standards while delivering measurable impact for clients and

Qualifications

5+ years of experience in software, data, or ML engineering in product-centric settings.
Experience in production-level Python and building backend services and APIs.
Experience integrating LLMs in production and handling their practical limits: context windows, cost, latency, and non-determinism.
Experience building agent harnesses and multi-agent, autonomous agent, or agent-to-agent systems in production.
Experience setting up evaluation and metrics for AI systems, and using them to make decisions.
Familiarity with MLOps practices, containers, CI/CD, and at least one major cloud platform.
Excellent communication skills and ability to work collaboratively in a fast-moving environment.

Responsibilities

Own data pipelines behind the AI platform, turning register data and web content into parsed, structured data with an audit trail and data-quality checks.
Build the context management layer that controls what our agents and LLMs see, balancing relevance, budgets, memory and cost.
Build the harnesses our agents run in: the agent loop, tool calling, context assembly, guardrails and tracing.
Design and build multi-agent and autonomous agent systems, including agent-to-agent interfaces for reliable work handoffs.
Develop evaluation pipelines and production metrics for LLM and data quality to measure changes before and after shipping.
Run and harden our LLM integrations in production with fallbacks, rate limiting, caching and observability.
Set up MLOps practices such as CI/CD for ML components, versioning of prompts, models, and datasets, and safe rollouts.
Work closely with ML and backend engineers to turn prototypes into reliable production services with clean interfaces.
Continuously improve engineering, data, and AI practices, defining standards, processes, and tooling.

Skills

Software, data, or ML engineer

Python backend

LLMs in production

Multi-agent systems

MLOps practices

Containers

CI/CD

Cloud platforms (AWS/GCP/Azure)

Communication skills

Education

Bachelor's or Master's degree in Computer Science or related field

Tools

Docker

Kubernetes

CI/CD tooling

Job description

About us

Condukt is the next-generation compliance platform for financial services, offering an agentic solution powered by real-time data for business identity.

Always-on and policy-aware, Condukt enables financial institutions and fintechs to gain actionable insights into business verification - unlocking growth, increasing efficiency, and strengthening compliance.

We were founded by former operators and builders with deep experience in banking, payments, and lending. Today, our team spans across London and Porto, bringing together engineers, product owners, and operators from companies like Revolut, Meta, Block (Square), SumUp, American Express, Yandex, and OakNorth.

Trusted by leading regulated entities, including Wise, Tide, Mollie, Shift4, Flatpay, and myPOS, Condukt is backed by top investors, including Lightspeed Venture Partners and MMC Ventures.

Position Overview

We are looking for a Senior AI Engineer to join our team. You will own the systems that connect our models to our product: the data pipelines that feed them, the context they work with, the multi-agent systems they run in, and the metrics that tell us whether they work. You'll set the standards and patterns our team builds on and work closely with our ML and backend engineers to take AI functionality from prototype to reliable, production-grade systems.

As an early-stage startup, we pride ourselves on our ability to roll up our sleeves and build from scratch. You’ll ship code frequently, iterate quickly based on feedback, and still meet the stringent security, performance, and uptime requirements our clients expect.

We all love to build, scale, and grow: our leadership team has a strong track record of building products, scaling teams, and driving growth, and our ambitious engineers join us from leading tech companies and unicorns.

If you’re passionate about building the foundations that make AI products reliable, measurable, and fast to iterate on, this role is for you. You’ll have a meaningful opportunity to shape our engineering culture from the ground up.

What you’ll do

Own the data pipelines behind our AI platform end to end, turning register data and web content into parsed, structured data with a full audit trail and strong data-quality checks
Build the context management layer that decides what our agents and LLMs see, balancing relevance, token budgets, memory, and cost
Build the harnesses our agents run in: the agent loop, tool calling, context assembly, guardrails and tracing
Design and build multi-agent and autonomous agent systems, including the agent-to-agent interfaces that let our agents hand work to each other reliably
Develop evaluation pipelines and production metrics for LLM and data quality, so every change to a model, prompt, or pipeline can be measured before and after it ships
Run and harden our LLM integrations in production, with robust fallbacks, rate limiting, caching, cost tracking, and observability
Set up MLOps practices such as CI/CD for ML components, versioning of prompts, models, and datasets, and safe rollouts
Work closely with ML and backend engineers to turn prototypes into reliable production services with clean interfaces
Continuously improve our engineering, data, and AI practices, helping define standards, processes, and tooling that align with industry best practices

What you bring

You are an effective and self-driven builder who enjoys working across the boundary between ML, backend engineering, and DevOps
5+ years of experience as a software, data, or ML engineer in product-centric settings
Experience in production-level Python and building backend services and APIs
Experience integrating LLMs in production and handling their practical limits: context windows, cost, latency, and non-determinism
Experience building agent harnesses and multi-agent, autonomous agent, or agent-to-agent systems in production
Experience setting up evaluation and metrics for AI systems, and using them to make decisions
Familiarity with MLOps practices, containers, CI/CD, and at least one major cloud platform
Experience self-hosting open-weight models is a plus
Bachelor's or Master's degree in Computer Science or a related field
Excellent communication skills and ability to work collaboratively in a fast-moving environment