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Company Overview
A leading financial technology and innovation company is seeking a Lead Machine Learning Engineer to drive the design, development, and deployment of cutting-edge Generative AI applications and agentic workflow systems. The organization focuses on cloud-native ML solutions, scalable infrastructure, and responsible AI practices to deliver high-impact analytics and automation across multiple business domains.
Summary
The Lead Machine Learning Engineer will participate in the technical design, development, and production of complex machine learning applications at scale. The role requires building robust ML serving architectures, developing high-performance application code, and ensuring the reliability, security, and low latency of Generative AI solutions. Collaboration with multiple AI/ML teams and application of best practices in machine learning engineering is key.
Responsibilities
Design, build, and deliver Generative AI models and components to solve complex business problems in collaboration with Product and Data Science teams
Implement cloud-native ML Serving platforms using technologies like Docker, Kubernetes, KNative, and KServe
Develop and test high-performance application code in Python and Go to address scaling and high-availability challenges
Apply advanced MLOps and GitOps practices, including CI/CD pipelines with tools like ArgoCD
Leverage service mesh architectures (e.g., Istio) to manage traffic, security, and resilience for high-volume ML endpoints
Retrain, maintain, and monitor models in production environments
Construct optimized and scalable data pipelines to feed ML models
Ensure code quality, model governance, and adherence to Responsible and Explainable AI best practices
Collaborate with cross-functional teams to continuously improve ML systems and workflows
Qualifications
Basic Qualifications
Bachelor’s Degree in Computer Science, Electrical Engineering, or related field
Minimum 6 years designing and building data-intensive solutions using distributed computing (internship experience does not apply)
At least 4 years of programming experience with Python, Scala, Go, or Java
Minimum 2 years building, scaling, and optimizing ML systems
Preferred Qualifications
Master’s or Doctoral Degree in a relevant field
3+ years building production-ready data pipelines for ML models
3+ years working with ML frameworks such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow
2+ years developing performant, resilient, maintainable code
2+ years of experience with data gathering and preparation for ML models
2+ years of people leadership experience
1+ years leading teams developing ML solutions using best practices and automation
Experience deploying ML solutions on public cloud platforms such as AWS, Azure, or Google Cloud
Demonstrated impact in the ML industry via conferences, publications, open-source contributions, or patents
Disclaimer
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1. No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality. 2. No Client Relationship: This company is not a client of SpringCube unless stated. 3. To Apply: Click the “Apply” button to be redirected to the hiring company’s application page for this job. 4. No Liability: SpringCube is not liable for inaccuracies.
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