Job Title: Lead Machine Learning Engineer
Summary/Objective
The Machine Learning Engineering function is responsible for ensuring Smarsh can run state-of-the-art enterprise-grade machine learning at scale in a cost-effective manner. The Machine Learning Engineering Group develops and maintains the ML infrastructure, tooling, and analytic services to power intelligent applications.
Job Description
As a Lead Machine Learning Engineer, you will play a pivotal role in leading and driving the implementation of advanced analytics that delivers communications intelligence that will drive actionable insights and solutions as part of our FinTech and RegTech Product portfolio. You will be part of a cross-functional agile team of talented ML engineers collaborating closely with product managers, data scientists and other stakeholders to deliver high quality, secure and resilient SaaS products.
Essential Functions
Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
Essential Functions below:
- Collaborate with cross-functional teams to design complex solutions and integrate multiple software products to solve high-level challenges.
- Act as a subject matter expert in machine learning, fintech, and Regtech domains, providing technical leadership, insights, and guidance to internal teams and external stakeholders.
- Can weigh the pros and cons of various solutions and propose the best path.
- Work closely with Product Management and engineering teams to define and implement features in a fast-paced environment with careful attention to quality, scalability, and maintainability.
- Can break down complex technical solutions into simple abstractions.
- Influence Principal Engineers/Product Managers on product designs.
- Can investigate and solve complex bugs, performance, security and scalability issues.
- Actively participate in troubleshooting and fixing Production Issues following the incident management process.
- Recognize issue patterns and implement proactive measures to address the root causes.
- Manage task lifecycle using tools like JIRA
- Participate in internal & external code reviews, provide and receive feedback for continuous improvement.
- Influence, Establish and Sustain Best Practices.
- Mentor and coach team members.
- Actively participate in team agile ceremonies and provide valuable inputs.
- Other duties as assigned.
Education and Experience
- Minimum 8+ years industry experience.
- BS in CS/Masters in CS
- Or equivalent combination of education and experience.
Technical Requirements
- Proficiency with JVM language (Java/Kotlin) and experience in Python
- Experience in NLP(including LLMs, MLMs), ML-Ops and data pipelines
- Experience with ML frameworks/libraries such as TensorFlow, PyTorch, scikit-learn
- Strong understanding of ML Algorithms, Statistical techniques, and data analysis methodologies
- Experience with Data processing, feature engineering and model evaluation techniques.
- Experience in cloud platforms like Amazon Web Services & Google Cloud
- Experience with Amazon Sagemaker and Jupyter Notebooks
- Experience with Model Servers such as Triton Inference server
- Experience working in AI/ML based Analytics products for Fintech/Regtech domain
- Experience in microservices & event-driven architecture.
- Exposure and experience in building ML applications/services with cloud scalability
- Experience in Kafka and RDBMS such as MySQL & Postgres
- Proficient in containerized platforms like Docker, Helm & Kubernetes
- Experience in CI/CD tools like Bamboo, ArgoCD
- Experience in Prometheus & Grafana
- Proficient in API design
- Proficient in working with distributed systems
Additional Eligibility Qualifications
- Expert programming skills in relevant languages.
- Strong analytical, design, problem solving skills and customer focus.
- Strong communication and collaboration skills.
- Good organizational skills.
- Deep understanding and experience in software architecture/software engineering.
- Strong understanding and experience in continuous software delivery.
- Strong understanding of ML business and technology domain.
