Lead Software Engineer

About Solstice Analytics Solstice Analytics prevents SaaS data breaches by delivering end-to-end SaaS security. Our platform gives security teams clear visibility into posture, access, third-party connections, AI-related activity, and with built-in discovery to identify unsanctioned SaaS and Shadow AI tools. Backed by continuous monitoring and real-time threat detection, Solstice Analytics helps enterprises identify and resolve risks early, keeping their SaaS applications secure. Recognized as a Frost Radar™ 2025 Leader and Great Place To Work ® , Solstice Analytics continues to set the standard for innovation and customer value in SaaS security. The largest and fastest-growing global enterprises across industries trust Solstice Analytics to secure their SaaS applications. About the Role The Lead Software Engineer plays a key role in the company’s AI strategy. This role offers the opportunity to make a meaningful impact across the whole platform. The Lead Software Engineer (backend) collaborates closely with Product, Engineering, and Field teams to implement different AI applications. This position focuses on development, implementation, testing and monitoring of our GenAI infrastructure and deployment pipelines. What You’ll Do Design, build, and maintain backend services and APIs to support our GenAI product using Python and FastAPI, ensuring they are scalable, reliable, and secure. Develop and operate LLM-powered applications and agentic workflows using LangChain, LangGraph, and LangSmith, including tracing, evaluation, and observability. Develop and manage infrastructure using Terraform, applying infrastructure-as-code (IaC) practices across backend and ML resources. Build and maintain monitoring and alerting systems to surface service and API health issues early, ensuring reliability and performance. Handle large datasets to ensure data is processed, ready for model context, and collaborate in the platform data pipeline. Evaluate and select appropriate tools and technologies and define the overall technical strategy. Assess and interpret data to gain insights, especially for testing and monitoring purposes. Build and maintain CI/CD pipelines, including infrastructure-as-code (IaC), and automate deployment and monitoring across backend services and ML workflows. Continuously improve and automate monitoring and alerts processes. Optimize infrastructure and services for performance, scalability, reliability, and cost efficiency, and troubleshoot issues across deployment and runtime. Troubleshoot and resolve issues related to pipeline deployment and performance. Contribute to the development of internal engineering standards and documentation. Advocate for engineering best practices and promote a culture of continuous improvement also mentoring and guiding others in the company in this topic. Understand the requirements of stakeholders and translate them into technical solutions. Develop robust model monitoring and alerting systems to ensure model performance and identify issues. Support our mission and vision and embody and demonstrate Solstice Analytics core values of Trust, Transparency, Quality, Customer Focus, and Delivery. Comply with all company policies and governing laws and regulations. What We’re Looking For At least 8 years of experience in backend centric software development. Degree in a relevant field such as Engineering, Computer Science, Data Science, Machine Learning, or a related quantitative discipline. Strong programming skills: Proficiency in coding Python for production implementation, including coding best practices, testing, performance considerations, with a good understanding of libraries like Pandas, Polars, Langchain, Langsmith. Experience implementing infrastructure for Machine Learning and Generative AI applications. Experience in security engineering or another complex domain. Excellent communication and collaboration skills to work effectively with Product, Engineering, Field, and other cross-functional teams. Experience in Cloud computing: Experience with cloud platforms like AWS, Azure, or GCP for model training and deployment. Experience with ML services in Cloud Platforms like VertexAI in GCP. Proficient in containerization technologies (Docker, Kubernetes) Experience with model monitoring and logging tools (e.g., Prometheus, Grafana, ELK stack). Usage of Software engineering practices: Version control, code review, and software design principles. Expertise in CI/CD tools (Github, Jenkins, GitLab CI, CircleCI). Experience with infrastructure-as-code tools (Terraform, CloudFormation). Problem-solving and critical thinking with the ability to analyze complex problems, identify potential issues, and develop innovative solutions. Strong self-management skills and able to prioritize tasks and manage time effectively. Proactive approach to work and ability to take initiative. Culture Our talented team is collaborative and supportive as we move quickly to research and develop new ideas, deliver new features to our customers, and iterate on id