WHO WE ARE:
Zinnia is the leading technology platform for accelerating life and annuities growth. With innovative enterprise solutions and data insights, Zinnia simplifies the experience of buying, selling, and administering insurance products. All of which enables more people to protect their financial futures. Our success is driven by a commitment to three core values: be bold, team up, deliver value – and that we do. Zinnia has over $180 billion in assets under administration, serves 100+ carrier clients, 2500 distributors and partners, and over 2 million policyholders.
WHO YOU ARE:
Zinnia continues to simplify the insurance industry by delivering innovative technology-driven experiences. Our talented software engineering team is dogma-free and experiment-oriented. We are relentless in our drive to reliably deliver outstanding products at scale. We can go further faster with experienced, collaborative, challenge-seeking engineers such as yourself.
What You’ll Do
As a Sr. Machine Learning Ops Platform Engineer, you will be responsible for building automation and leading-edge architecture around Data and AI/ML engineering on an AWS & Kubernetes platform. Specifically, you will code and help architect a production-grade, scalable platform. You will help define and ensure best coding & CICD practices within the team of excellent and engaged engineers. You will be given creative freedom and work in a supportive team environment. You will do hands-on code development and interact with business stakeholders.
- Build and maintain systems built using DevOps & LLM Ops (Kubernetes, Docker), AWS, Python and Terraform.
- Stay informed of industry trends and enable successful DevOps/MLops AWS platform and Terraform solutions by leveraging best practices.
- Participate in and set up Proof of Concepts (POCs) to demonstrate proposed solutions.
- Enable team members through training, culture, and team building.
- Identify, design, and implement internal process improvements: Automating manual processes, re-designing infrastructure for greater scalability, etc.
- Build infrastructure needed for AWS platform, such as Lambdas, EC2, Docker, pipeline engineering, data monitoring alerting, and networking.
- Experience in implementing observability stack like prometheus, loki, grafana/datadog
- Build, Design, Implement and support Data & ML models pipelines using latest CICD and deployment technologies.
- Work with stakeholders including the Executive, Product, Data and Design teams to help with technical issues and support their infrastructure needs.
- Participate in Code Review and process improvement.
- Let's talk about qualifications and experience:
- 7+ years of total experience in a complex, technical environment. Experience with developing production-grade code in Python, SQL & Pandas.
- Experience with 3 or more of the following AWS tools: Lambda, EC2, EMR, S3, Glue, Athena, RDS, Networking, IAM, Batch processing, Sagemaker, Airflow
- Experience or self-study in Terraform and, generally commonly used DevOps tools and techniques like Kubernetes , Docker, Github, Github Actions, Code pipeline & Jenkins.
- Experience in Kubeflow & MLFlow would be a good advantage.
- Experience in creating and working with CICD pipelines and APIs.
- Experience with relational SQL and NoSQL databases. Snowflake experience is plus.
- Experience working with AI/ML teams and cross-functional teams in a dynamic environment.
Qualifications
- Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or related field.
- 5–7+ years of professional experience in AI/ML development, ideally with ownership of production-level AI systems.
- Proven experience at an AI-first company or contributions to AI/ML research or open-source projects are highly desirable.
WHAT’S IN IT FOR YOU?
At Zinnia, you collaborate with smart, creative professionals who are dedicated to delivering cutting-edge technologies, deeper data insights, and enhanced services to transform how insurance is done. Visit our website at www.zinnia.com for more information. Apply by completing the online application on the careers section of our website. We are an Equal Opportunity employer committed to a diverse workforce. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability.
