Machine Learning Engineer
Endava Buenos Aires, Argentina
IT Services and IT Consulting · 10,001+ employees
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About the role
Deploy, operate, and monitor machine learning models as reliable, low-latency services for a mobile trading app, and build and schedule the data pipelines that support them. Integrate market-news and product analytics data, establish production ML deployment and monitoring practices, and document the systems and handover plan.
What they look for
Requirements
Requires at least four years of software or ML engineering experience, including models running in production, strong Python skills, and experience with model-serving APIs, containers, cloud infrastructure, and data orchestration. Candidates should also have experience with CI/CD, infrastructure as code, observability, and high-availability systems; financial services, feature stores, and ML platforms are advantageous.
Benefits
Full description
Company Description
Technology is our how. And people are our why. For over two decades, we have been harnessing technology to drive meaningful change.
By combining world-class engineering, industry expertise and a people-centric mindset, we consult and partner with leading brands from various industries to create dynamic platforms and intelligent digital experiences that drive innovation and transform businesses.
From prototype to real-world impact - be part of a global shift by doing work that matters.
Job Description
We are seeking ML Engineers to take machine learning models into production for our new mobile trading app. They will deploy, operate and monitor the models, and build the data pipelines those models depend on.
What they will do
- Deploy models as reliable, low-latency services consumed by the mobile app backend
- Build and schedule feature pipelines, including daily refresh of customer-level scores
- Integrate an external market-news feed (e.g. Refinitiv/LSEG) and product analytics data (Amplitude, in-app video engagement) into our data warehouse
- Implement model monitoring, drift detection, alerting and retraining workflows
- Establish deployment patterns, CI/CD and testing standards for ML in our production environment
- Document everything needed for long-term ownership and deliver a handover plan
Qualifications
Must have
- 4+ years in software or ML engineering, with models running in production
- Strong Python; model serving via REST/gRPC APIs, Docker and Kubernetes or equivalent
- AWS experience (e.g. S3, compute, managed ML services)
- Data pipelines and orchestration (Airflow or similar), including third-party API integration
- CI/CD, infrastructure-as-code and observability tooling
- Experience in high-availability environments where outages affect customers
Nice to have
- Financial services, brokerage or other regulated-industry experience
- Feature stores and ML platforms (e.g. MLflow, SageMaker, Feast)
- Market data or financial news feeds
Additional Information
Discover some of the global benefits that empower our people to become the best version of themselves:
- Finance: Competitive salary package, share plan, company performance bonuses, value-based recognition awards, referral bonus;
- Career Development: Career coaching, global career opportunities, non-linear career paths, internal development programmes for management and technical leadership;
- Learning Opportunities: Complex projects, rotations, internal tech communities, training, certifications, coaching, online learning platforms subscriptions, pass-it-on sessions, workshops, conferences;
- Work-Life Balance: Hybrid work and flexible working hours, employee assistance programme;
- Health: Global internal wellbeing programme, access to wellbeing apps;
- Community: Global internal tech communities, hobby clubs and interest groups, inclusion and diversity programmes, events and celebrations.
At Endava, we’re committed to creating an open, inclusive, and respectful environment where everyone feels safe, valued, and empowered to be their best. We welcome applications from people of all backgrounds, experiences, and perspectives—because we know that inclusive teams help us deliver smarter, more innovative solutions for our customers. Hiring decisions are based on merit, skills, qualifications, and potential. If you need adjustments or support during the recruitment process, please let us know.
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