Machine Learning Engineer
Pezesha
Quick Take
You'll build and deploy credit scoring models using alternative data, maintain ML pipelines in production, and collaborate with product and engineering teams to power AI-driven lending features.
At least 3 years of ML engineering experience with strong Python skills, hands-on MLOps knowledge (versioning, monitoring, retraining), and ideally some background in credit risk or financial data.
A highly competitive salary of up to KES 330,000/month, the chance to shape AI infrastructure at a fintech with real social impact across Africa's underserved SME market.
Job Description
Pezesha is on a mission to build Africa's embedded credit infrastructure, connecting lenders with underserved small and medium-sized enterprises across the continent. As the company continues to grow its fintech footprint, it is looking to bring on board a talented Machine Learning Engineer based in Nairobi to strengthen its credit intelligence capabilities.
In this role, you will sit at the intersection of data science and engineering, taking ownership of the full machine learning lifecycle — from model development and experimentation through to production deployment and ongoing performance monitoring. You will play a direct role in shaping how Pezesha assesses creditworthiness and delivers AI-powered features to its platform.
- Develop and continuously refine credit scoring models that leverage alternative data sources to assess borrower risk
- Deploy trained machine learning models into production environments and maintain robust monitoring to track their performance over time
- Design and maintain data pipelines that support the training, testing, and evaluation of models
- Stay current with emerging modelling techniques and investigate novel credit signals that could improve predictive accuracy
- Work closely with product and engineering teams to integrate AI-driven features into Pezesha's broader platform
- A minimum of three years of hands-on machine learning engineering experience in a professional setting
- Proficiency in Python along with widely used ML libraries including scikit-learn, XGBoost, and LightGBM
- Practical experience with MLOps practices, covering model versioning, performance monitoring, and retraining workflows
- Familiarity with feature stores and experiment tracking tools such as MLflow or DVC
- A background in credit risk or working with financial datasets is considered a strong advantage
This position is best suited to an ML engineer who is comfortable working across both research and production-grade environments. You thrive when given ownership of complex modelling problems and enjoy collaborating across technical and product teams to deliver real-world impact. If you have a particular interest in financial inclusion, alternative credit data, or the African fintech landscape, this role offers a compelling opportunity to apply your skills where they genuinely matter. Candidates with prior exposure to credit risk modelling or lending platforms will find themselves especially well positioned for this role.
Interested candidates should apply for the Machine Learning Engineer position at Pezesha through the job listing on this platform. Ensure your application clearly highlights your ML engineering experience, relevant tools and libraries you have worked with, and any background in financial or credit-related data. Applications should be submitted online as directed in the listing.
Requirements Breakdown
Must Have
- 3+ years of machine learning engineering experience
- Strong Python programming skills with ML libraries such as scikit-learn, XGBoost, and LightGBM
- Hands-on MLOps experience including model versioning, monitoring, and retraining pipelines
- Ability to build and maintain data pipelines for model training and evaluation
- Experience deploying ML models to production environments
Nice to Have
- Understanding of credit risk modelling or financial data
- Familiarity with feature stores and experiment tracking tools like MLflow or DVC
- Experience with alternative data sources for credit scoring
- Background working in fintech or financial services
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Salary Context
Above market rate for Machine Learning Engineers in Nairobi
ML Engineers in Nairobi typically earn between KES 120,000 and KES 220,000 per month depending on experience and sector, making Pezesha's upper range of KES 330,000 notably competitive and reflective of the specialised fintech and credit risk expertise required. Salaries in this field are driven upward by the scarcity of MLOps-proficient engineers and growing demand from fintech, BFSI, and global tech companies hiring locally.
About Pezesha
Pezesha is a Nairobi-based fintech building embedded credit infrastructure that connects lenders with underserved micro and small businesses across Africa. By leveraging alternative data and technology, Pezesha enables financial institutions and platforms to extend affordable credit to SMEs that traditional scoring models overlook. Backed by notable investors and operating across multiple African markets, Pezesha sits at the intersection of financial inclusion and cutting-edge AI — making it an attractive employer for engineers who want their work to have measurable real-world impact.
Likely Interview Questions
- 1
Can you walk us through a credit scoring or risk model you built end-to-end — from data sourcing to production deployment?
- 2
How have you handled model drift in a production ML system, and what monitoring strategies did you put in place?
- 3
What alternative data sources have you worked with, and how did you evaluate their predictive value for a financial use case?
- 4
Describe your experience with MLflow or DVC — how did you structure experiment tracking and model versioning on a past project?
- 5
How would you approach building a feature store from scratch for a credit scoring pipeline with multiple data sources?
Application Tips
Highlight any end-to-end ML projects in your CV — especially ones where you took a model from experimentation through to a live production environment with monitoring in place.
Emphasise experience with financial, transactional, or alternative data (e.g. mobile money, e-commerce behaviour, telco data) as this maps directly to Pezesha's credit infrastructure work.
If you have used MLflow, DVC, or any feature store, call these out explicitly with context on how they improved your team's workflow — this will set you apart from candidates with only notebook-level ML experience.
Career Path
Roles that lead here
Where this leads
Skills & Keywords
Honest Assessment
Green Flags
- Salary range is transparent and above the Nairobi market average for this experience level, signalling that Pezesha values this role competitively.
- The role spans the full ML lifecycle — research, engineering, deployment, and monitoring — offering genuine breadth and skill development rather than a siloed function.
- Pezesha operates in the high-impact financial inclusion space, giving engineers meaningful context for their work beyond pure tech metrics.
- Cross-functional collaboration with product and engineering teams suggests an integrated, product-minded ML culture rather than a back-office data team.
Watch Out
- The posting does not mention remote or hybrid work options, which may be a concern for candidates outside Nairobi or those expecting flexible arrangements.
- No mention of team size or existing ML infrastructure maturity — candidates should ask whether they'd be building from scratch or iterating on established systems.
A Day in the Life
A typical week at Pezesha might start with a Monday standup with the product and engineering squads to align on an upcoming credit feature, followed by time in Python iterating on a LightGBM model trained on mobile money transaction data. Midweek you'd be reviewing MLflow experiment runs to compare model versions, triaging a monitoring alert on a deployed scoring model whose input distribution has shifted, and writing a short internal memo on a new alternative data signal you've been researching. By Friday you'd likely be reviewing a pull request for a new data pipeline, and joining a broader team session to demo a prototype AI feature to stakeholders.
Frequently Asked Questions
What qualifications do I need to become a Machine Learning Engineer at Pezesha?
You need at least 3 years of ML engineering experience, strong Python skills with libraries like scikit-learn and XGBoost, and demonstrated MLOps experience including deploying and monitoring models in production. A background in credit risk or financial data is a strong advantage but not strictly required.
Is the Machine Learning Engineer role at Pezesha remote or office-based?
The posting lists the location as Nairobi with no mention of remote or hybrid options, so candidates should assume an in-office or Nairobi-based arrangement and confirm working arrangements directly with the company during the interview process.
How much does a Machine Learning Engineer earn at Pezesha?
Pezesha is offering a salary of KES 200,000 to KES 330,000 per month, which is above the typical Nairobi market rate for ML Engineers and reflects the specialised fintech and MLOps expertise required for the role.
What are the career growth opportunities for a Machine Learning Engineer at Pezesha?
Given the breadth of the role — spanning model research, production deployment, and cross-functional AI feature development — strong performers would be well-positioned to grow into a Senior or Lead ML Engineer role, or eventually move into a Head of Data Science or AI product leadership position as the company scales across Africa.
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