Job Description

Tiko is currently hiring a AI / ML Engineer to join their team

About Tiko

Tiko is an African nonprofit committed to strengthening the potential and resilience of adolescent girls across Africa. They address the “Triple Threat” of unintended pregnancy, HIV infection, and sexual and gender-based violence by building local health ecosystems that provide stigma-free, no-cost, quality-assured services.

They operate in six countries: Kenya, Ethiopia, Uganda, Burkina Faso, South Africa, and Nigeria, with additional offices in Portugal, the Netherlands, and the United Kingdom. For a clear overview of our work, we recommend watching this short video.

Globally, their team consists of +250 enthusiastic, international colleagues. Whether you are working from our biggest office in Nairobi, the fast-growing office in South Africa, or from home, our people are young, and our culture is global and dynamic. Their work environment is fast-paced, informal, and friendly.

For this position, they are happily looking for candidates currently based in Portugal.

The Job

They are looking for a T-Shaped AI / ML Engineer to join a high-ownership, agile team tackling one of their most critical scaling challenges: risk and fraud on the Tiko platform. In their engineering ecosystem, "T-shaped" isn't a buzzword- it means you bring a broad, rock-solid foundation in software engineering and data infrastructure (the horizontal bar), combined with deep, hands-on mastery of deploying modern AI and LLM-based systems in production (the vertical bar).

You will move incredibly fast to build, monitor, and optimise intelligent systems that protect their platform. This is a role where you will own entire outcomes from day one, ensuring their architectures remain scalable, secure, and performant for the communities we serve.

Responsibilities

Agentic Systems & GenAI Engineering (The Vertical Bar)

• Agent Orchestration: Design, deploy, and debug sophisticated agentic AI workflows, focusing on multi-step reasoning, memory integration, Model Context Protocol (MCP), and multi-agent frameworks (using LangGraph, LangChain, or equivalent setups).
• Advanced RAG Pipelines: Architect and refine retrieval-augmented generation systems, continually optimising chunking strategies, retrieval design, and end-to-end pipeline evaluation.
• LLM Serving & Optimisation: Deploy and operate LLM-based services directly in production, aggressively managing runtime latency, throughput, and cloud costs (using tooling like LiteLLM, vLLM, SageMaker, or Bedrock).

Production Foundations & ML Rigour (The Horizontal Bar)

• Traditional Grounding: Implement robust traditional machine learning models—specifically across tabular/structured data (classification, regression, ranking) or NLP—to solve complex risk and behavioral patterns.
• Production Coding: Write idiomatic, clean, and production-quality Python code that seamlessly integrates into their core data and software engineering ecosystems.
• Structured Data: Query, manipulate, and pipe structured data at scale using complex SQL configurations.
• Iterative Systems Design: Participate in team rituals, maintain meticulous technical documentation, and champion a culture of shared learning and graceful systems degradation.

Requirements

• Professional Track Record: 5+ years of experience operating as an ML Engineer, Data Scientist, AI Engineer, or Software Developer with a clear history of shipping code to production.
• The Software Core: Outstanding proficiency in Python and deep experience working with large-scale structured data using SQL.
• Production GenAI: Proven, practical experience building and serving live LLM-based or agentic systems for real users (not just internal prototypes).
• ML Breadth: Solid grounding in traditional machine learning fields, with distinct depth in at least one area (tabular ML, NLP, or deep learning).
• Language: Strong verbal and written English communication skills.

Bonus Points:

• Direct domain experience in Risk, Fraud, Credit, Insurance, Cybersecurity, or Fintech ecosystems.
• Experience with GenAI observability, drift detection, and evaluation frameworks (e.g., LangSmith, Evidently, RAGAS)
• Hands-on mastery of AWS environments and MLOps automation (Containerisation, CI/CD for ML)
• A track record of mentoring juniors or contributing to the wider community through technical blogging or public speaking.

Recruitment Process

Introductory call with recruiter | First Interview with Tech Team | Technical Challenge | Final interview with the Tech Team

The Rewards

We will offer you a salary in this bracket depending on your level of experience and how it relates to your future colleagues.

In addition to your monthly salary, we offer you:

• Benefits and allowances tailored to your location.
• Flexible work arrangements, including remote or hybrid options.
• A personal development budget of €500 per year to invest in your professional growth
• Unlimited holiday days to use as you see fit - just coordinate with your team and take the time you need to recharge.
• The opportunity to shape a growing, impactful product and leave your mark on how they work
• A culture built on trust - we believe you’ll do your best without the need for unnecessary rules or micromanagement.

Want to know more? Get in touch with us 👇