Full Stack AI Engineer

Role & responsibilities

• Design and build production-grade AI applications using modern frontend, backend, and GenAI technologies.
• Develop intuitive user experiences for conversational AI, analytics, agent workflows, and decision-support applications.
• Build scalable backend APIs and services for AI-powered applications using Python-based frameworks such as FastAPI.
• Integrate LLMs and AI models from providers such as Anthropic, OpenAI, Azure OpenAI, and open-source models.
• Build and integrate RAG pipelines, knowledge retrieval, semantic search, tool calling, and multi-agent workflows.
• Develop capabilities that allow agents to interact securely with enterprise data, APIs, applications, and analytical workflows.
• Work with structured and unstructured data across Snowflake, Databricks, relational databases, object stores, and vector databases.
• Implement conversational and agentic workflows that can reason across data, documents, business rules, and domain-specific knowledge.
• Develop reusable UI components and product frameworks for multiple ciATHENA applications and use cases.
• Implement authentication, authorization, SSO, RBAC, audit logging, and enterprise security controls.
• Build AI observability, evaluation, tracing, monitoring, and feedback mechanisms.
• Optimize AI applications for performance, reliability, latency, and cost.
• Containerize and deploy services using Docker and cloud-native technologies.
• Participate in architecture decisions, code reviews, testing, CI/CD, and production support.
• Work closely with Forward-Deployed AI Engineers to rapidly configure and deploy customer-specific use cases.
• Translate product requirements and business workflows into working software with minimal handoffs.

Preferred candidate profile

• 4-6 years of software engineering experience with strong full-stack development capability.
• At least 1-2 years of hands-on experience building applications using LLMs or GenAI technologies.
• Strong Python development skills.
• Strong experience with React, Next.js, TypeScript, or equivalent modern frontend frameworks.
• Experience building APIs, backend services, and distributed applications.
• Hands-on experience integrating LLMs into production applications.
• Understanding of RAG, embeddings, vector search, prompt design, and agent architectures.
• Ability to work with enterprise data platforms and relational databases.
• Strong understanding of software engineering fundamentals, design patterns, testing, and maintainability.
• Experience building secure enterprise applications with authentication and authorization.