Lead the design and development of core components of our next-generation analytics platform with a growing suite of AI-powered applications. Guide a team of engineers, own key subsystems, and drive integration of AI/ML capabilities.

About the Company

Improzo (a portmanteau of "Improve" and "Zoe," the Greek word for life) is a young, fast-growing technology firm dedicated to improving lives by empowering clients in the life sciences sector with quality-led commercial analytical solutions. Founded by seasoned industry leaders, Improzo brings together a team of experts in commercial data, technology, and operations who have been evolving and learning together since inception. The company's culture is defined by its CARE values: Customer-Centric (client success is our success), Adaptive (agile, innovative, growth mindset), Respect (collaboration, transparency, ethical responsibility), and Execution (laser focus on quality-led delivery). At Improzo, you won't be confined to a cubicle; you'll navigate open waters, collaborating with brilliant minds to shape the future of life sciences analytics.

Role Overview

We are seeking an experienced and hands-on Lead Software Engineer to lead the design and development of core components of our next-generation analytics platform, which includes a growing suite of AI-powered applications. In this critical role, you will combine deep technical expertise with people leadership, guiding a team of engineers to build scalable, high-quality solutions in the data analytics and AI/ML space specifically for the life sciences industry. You will translate platform architecture into robust implementations, drive engineering best practices, and ensure timely, high-impact delivery of features that empower users across the product launch, segmentation, brand management, sales force effectiveness, and reporting & dashboards value chain.

Key Responsibilities

• Lead the design and development of key platform components for an AI-first analytics and applications platform.
• Act as a technical owner for major subsystems, ensuring alignment with overall platform architecture and product vision.
• Guide and mentor a team of software engineers specializing in Python (Django/Flask), data engineering, and AI/ML.
• Collaborate closely with Product Architects, Product Managers, and business stakeholders to translate architectural direction and requirements into high-quality technical designs and implementations.
• Drive the development and integration of advanced AI/ML and generative AI capabilities (LLMs, agents, workflows) into the platform for use cases such as launch planning, field & HCP engagement, segmentation, and personalization.
• Build and maintain scalable data pipelines and backend services using modern big-data technologies (Spark, Kafka, Databricks, Snowflake, etc.).
• Establish and enforce engineering best practices around coding standards, testing, security, performance, and observability.
• Own hands-on development, code reviews, and technical design reviews to ensure high-quality, maintainable solutions.
• Oversee CI/CD pipelines, DevOps practices, and cloud-native deployments using Docker, Kubernetes, and automated release workflows.
• Partner with data scientists and ML engineers to operationalize, deploy, and monitor AI/ML models in production.
• Foster a collaborative, Agile, and delivery-focused engineering culture that balances speed with quality.
• Contribute to hiring, onboarding, and performance development of engineering team members.
• Stay current with emerging technologies and trends, applying them pragmatically to product development.

Tech Stack

• Backend: Python, Django, Flask
• Data Engineering & Big Data: Spark, Kafka, Airbyte, Databricks, Snowflake, SQL/NoSQL databases, ETL/ELT pipelines, data warehousing
• Frontend: React, Angular, or Vue.js (experience with at least one)
• Cloud: AWS (preferred), Azure, or GCP
• DevOps & CI/CD: Jenkins, GitLab CI, TeamCity, Docker, Kubernetes, Prometheus, Grafana, ELK stack
• AI/ML: LLMs, agent frameworks (e.g., CrewAI, LangChain), model deployment & monitoring
• Other: Microservices architecture, security & compliance (HIPAA, CCPA, SOC), life sciences commercial datasets (IQVIA, Symphony, Komodo, Veeva, OCE, etc.)

Bachelor's or Master's degree in Computer Science, Engineering, or a related discipline. 6–10+ years of hands-on experience in building and scaling software products, with at least 2–3 years in a technical leadership or lead engineer role. Strong proficiency in Python and backend frameworks such as Django or Flask. Solid experience with data engineering and big-data ecosystems, including SQL/NoSQL databases, Spark, Kafka, Airbyte, Databricks, Snowflake, or similar technologies. Good understanding of data modeling, ETL/ELT pipelines, and data warehousing concepts. Experience working with at least one modern front-end framework (React, Angular, Vue.js, etc.) and collaborating closely with UI teams. Hands-on experience with cloud platforms (AWS preferred; Azure or GCP acceptable). Practical experience with CI/CD tools (Jenkins, GitLab CI, TeamCity), containerization (Docker, Kubernetes), and monitoring/logging solutions (Prometheus, Grafana, ELK stack). Exposure to integrating AI/ML models into applications; experience with generative AI, LLMs, and agent frameworks (e.g., CrewAI, LangChain) is a strong plus. Familiarity with security, compliance, and data privacy considerations (HIPAA, CCPA, SOC) in regulated environments. Knowledge of life sciences or biopharma domain and commercial datasets (IQVIA, Symphony, Komodo, Veeva, OCE, etc.) is highly desirable. Strong problem-solving skills with a pragmatic, delivery-oriented mindset. Excellent communication skills and the ability to influence across engineering, product, and business teams. High attention to detail, strong ownership mindset, and a bias for quality and customer impact.

Competitive salary and comprehensive benefits package. Opportunity to lead and build cutting-edge analytics and AI-driven products for the Life Sciences industry. High-impact role with strong influence on technical direction and product outcomes. Collaborative, inclusive, and growth-oriented engineering culture. Opportunities for continuous learning, leadership development, and career progression.