Senior Software Engineer (R14284)

India · Remote Employee - India · 5+ years experience · Remote · Posted Oct 5, 2026

At a glance

The Machine Learning Platform team at Oportun builds software foundations that enable data scientists and machine learning engineers to develop, validate, deploy, and operate models safely and efficiently in production.
In this role, you will lead complex platform work from business context through production outcomes, combining hands-on engineering, architectural judgment, production ownership, and mentorship.
You will partner across Data Science, Product, Risk, Fraud, and Engineering to solve ambiguous problems and move the needle for members and the business.

Lead the design, implementation, deployment, and operation of complex platform capabilities that support the machine-learning lifecycle.
Design scalable systems for model deployment and serving, feature computation, data validation, workflow orchestration, monitoring, and production support.
Build reusable platform components, standards, and automation while improving reliability, scalability, observability, and operational readiness across ML systems.
Partner with Data Science, Product, Risk, Fraud, and Engineering teams to translate ambiguous business needs into practical technical solutions.
Own significant technical initiatives end to end and lead complex production investigations—clarifying requirements, leading design, delivering safely, measuring results, and implementing durable fixes.
Raise engineering quality through thoughtful code and design reviews, mentor engineers through pairing and technical guidance, and contribute to platform technical strategy.
Look for ways to use AI tools thoughtfully to work smarter, reduce manual work, and make better decisions faster.

5+ years of professional software-engineering experience building and operating production systems, with strong programming skills in Python and deep knowledge of API design, distributed systems, service architecture, testing, and software-design fundamentals.
Demonstrated experience designing and operating reliable, scalable cloud-native services or data-intensive systems, with experience in ML platforms, model serving, feature engineering, data platforms, MLOps, or related systems.
Practical experience with SQL, databases, caching, and tracing data or requests across multiple systems, plus familiarity with the ML lifecycle from feature engineering and model training through evaluation, deployment, serving, monitoring, and governance.
Experience with Docker, Kubernetes, CI/CD, Git-based workflows, and production observability, and ability to evaluate technical alternatives and clearly communicate recommendations, trade-offs, risks, and dependencies.
Demonstrated ability to mentor engineers, improve engineering practices, lead through technical influence, and connect engineering work to member, business, product, and operational outcomes.

Experience with AWS, Databricks, MLflow, SageMaker, Spark/PySpark, Kafka, or streaming systems.
Experience building feature platforms, model-serving systems, workflow-orchestration platforms, or internal developer platforms.
Familiarity with monitoring model quality, data quality, feature coverage, latency, reliability, or cost efficiency, and experience working with sensitive data in a regulated or financial-services environment.
Experience with Java or another backend language in addition to Python, and familiarity with AI tools such as ChatGPT or Copilot to improve personal productivity.

Can be performed as a Remote Employee based in India, with consideration for candidates open to relocating to India.

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