This role is responsible for architecting, building, and validating the next generation of Nitro AI Agents. We are looking for a seasoned engineer who lives at the intersection of robust software engineering and cutting-edge AI. You will go beyond simple prompting to design complex agentic workflows, get insights from large-scale repositories, and ensure the reliability of these systems within a mission-critical life sciences environment. You will enhance and add to existing Java-based back-end systems.
Agentic Architecture: Proven experience building scalable AI orchestration layers that drive operational workflows, ranging from high-precision Text-to-SQL agents to complex multi-agent systems capable of tool-calling, event orchestration, and autonomous self-correction
Model Strategy: Choose and configure the optimal LLMs based on cost, reasoning depth, and latency
Hybrid Data Systems: Build scalable bridges between Relational Databases (Postgres/Java) and Vector Stores, using metadata strategies like PageIndex to ensure data stays synchronized and searchable
Text-to-SQL Agents: Develop high-precision agents that translate natural language into complex SQL, featuring self-correction loops to handle large enterprise schemas accurately. Choose appropriate RAG approach for semantic embedding and retrieval
Automated Validation: Develop, implement, and maintain scalable automated evaluations to ensure agent behavior remains consistent across model updates and feature releases
Agentic Workflow Mastery: 2+ years of proven experience building scalable AI orchestration layers that drive workflows, ranging from precision Text-to-SQL agents to complex multi-agent systems capable of tool-calling, event orchestration, and autonomous self-correction
Scalable backend systems for AI orchestration: 7+ years of experience building and deploying distributed systems that handle high concurrency, rate-limiting, and asynchronous task queues using Java, Spring, and Python. Optimize AI orchestration for performance, scalability, and efficiency
RAG & Vector Expertise: Expert at building high-precision RAG systems across structured relational data and unstructured documents, utilizing vector databases to enable accurate retrieval across large-scale enterprise datasets
Automated Evaluation: Experience building pipelines to measure complex AI agent performance using key metrics like task success rate, accuracy, and output quality
AI Trends: Stay updated on the latest AI and machine learning advancements, research papers, and tools, incorporating them into AI development projects
Life Sciences Experience (nice to have): Familiarity with the unique data privacy and regulatory requirements of the life sciences industry
Mentorship: Demonstrated ability to mentor team members and contribute to a positive and high-performing team environment
Education: Bachelor’s degree in Computer Science, Data Science, Machine Learning, or a related technical field
Culture: High work ethic, high integrity, and a "do the right thing" mindset
Applicants must have the unrestricted right to work in the United States. Veeva will not provide sponsorship at this time
Medical, dental, vision, and basic life insurance
Flexible PTO and company paid holidays
Retirement programs
1% charitable giving program
Base pay: $110,000 - $270,000
The salary range listed here has been provided to comply with local regulations and represents a potential base salary range for this role. Please note that actual salaries may vary within the range above or below, depending on experience and location. We look at compensation for each individual and base our offer on your unique qualifications, experience, and expected contributions. This position may also be eligible for other types of compensation in addition to base salary, such as variable bonus and/or stock bonus.