Senior Data and AI Engineer at Panacea Financial

About this role

Company Overview
Panacea Financial Holdings (“Panacea”) is a rapidly growing fintech and banking platform purpose-built to serve the unique financial needs of physicians, dentists, and veterinarians. Panacea is building the next-generation financial ecosystem for healthcare professionals—spanning practice finance, personal banking, patient finance, accounting / tax, advisory and payments.

Position Summary
Panacea Financial Holdings is seeking a Senior Data and AI Engineer to design, develop, and maintain scalable AI solutions and the data infrastructure supporting our financial technology platform.
This role will play a key part in advancing Empower, our conversational AI platform, and developing internal AI applications that support business operations and decision making. The ideal candidate brings strong experience in AI engineering, data engineering, and cloud infrastructure, with the ability to build reliable, secure, and production-ready solutions.
Working within a collaborative technical team, this individual will help establish engineering standards, improve AI performance, and ensure solutions meet the security and compliance requirements of a regulated financial environment.

Key
AI Engineering & Development

Design, build, and maintain AI applications, including conversational AI, AI agents and agentic workflows, model integrations, and governed tools using Model Context Protocol (MCP) or similar frameworks.
Develop and optimize prompts, AI workflows, and evaluation processes to ensure accurate, reliable, and efficient outputs.
Leverage agentic coding tools to accelerate development while maintaining strong code quality, testing, and human review standards.
Partner with Product and business stakeholders to translate requirements into practical AI solutions.

Data Engineering & Governance

Develop and maintain data pipelines, integrations, and governed data models using Snowflake, SQL, dbt, and related technologies.
Support data quality, consistency, and customer data integration across systems.
Implement appropriate data access controls, security measures, and governance practices to protect sensitive information.

Cloud Infrastructure & AI Operations

Deploy, monitor, and maintain AI and data services in cloud environments, primarily Azure and Snowflake.
Develop and maintain CI/CD pipelines, infrastructure automation, and reliable release processes.
Monitor system performance, reliability, latency, and token and compute costs using LLM observability tools, identifying opportunities for improvement.
Collaborate with IT and engineering teams to maintain secure infrastructure and operational standards.

Testing, Evaluation & Continuous Improvement

Develop testing and evaluation frameworks to assess AI accuracy, performance, and reliability.
Identify and resolve technical issues through testing, monitoring, and user feedback.
Maintain documentation, technical standards, and processes that support responsible AI development and deployment.

Collaboration & Technical Leadership

Serve as a technical resource for AI engineering and related development initiatives.
Collaborate with engineering, analytics, IT, Product, and business teams on cross-functional projects.
Mentor colleagues, share best practices, and contribute to continuous improvement of engineering standards.
Communicate technical concepts and recommendations clearly to technical and nontechnical stakeholders.


Qualifications & Experience
Required

6+ years of relevant experience in AI engineering, software engineering, data engineering, cloud infrastructure, or related fields.
Bachelor's degree in computer science, engineering, or a related field, or equivalent practical experience.
Hands-on experience developing, deploying, and maintaining AI or large language model (LLM) applications in production environments.
Strong proficiency in Python and SQL, with experience in TypeScript/React or comparable frameworks.
Experience with cloud data platforms such as Snowflake and data transformation tools such as dbt.
Experience with AI and agent frameworks, retrieval-augmented generation (RAG), tool integrations, prompt engineering, and evaluation methodologies.
Hands-on experience with agentic development tools such as Claude Code, OpenAI Codex, Cursor, Devin, or Pi, including reviewing, testing, and validating AI-generated code.
Experience with cloud infrastructure, preferably Azure, including CI/CD pipelines, agent harnesses and orchestration, infrastructure as code, and containerized applications.
Understanding of data governance, security, access controls, and responsible AI practices.

Preferred

Experience with MCP, Azure AI Foundry, Snowflake Cortex, or similar AI platforms.
Experience with data ingestion, change data capture, entity resolution, or semantic data modeling.
Familiarity with identity management, secrets management, and cloud network security.
Experience in financial services, banking, healthcare, or other regulated industries.
Experience mentoring engineers or leading technical initiatives.

Originally posted on Himalayas