Description

 Design, prototype, build, test, and deploy production-ready AI capabilities, moving use cases from experimentation through integration into the PARROT application.

Provide hands-on technical leadership throughout the AI development lifecycle, including solution design, model and platform integration, evaluation, testing, deployment, monitoring, and ongoing improvement.

Act as a technical liaison between the PARROT team and the AEGIS AI Platform, helping the team understand and adopt available platform capabilities rather than developing unnecessary PARROT-specific AI infrastructure.

Establish reusable patterns and reference implementations for integrating LLM and Agentic AI capabilities into PARROT's existing services and workflows.

Upskill PARROT developers through pairing, code reviews, technical workshops, documentation, examples, and hands-on development so that AI capabilities can increasingly be owned and maintained by the core team.

Establish practical engineering practices for AI development, including prompt management, structured outputs, tool use, agent orchestration, evaluation, observability, error handling, and testing.

Evaluate technical tradeoffs between traditional software solutions, deterministic automation, LLM-based solutions, and agentic approaches, applying AI only where it provides meaningful product or engineering value.

Collaborate with product, engineering, analytics, architecture, and AI Platform teams to ensure AI solutions meet business requirements while remaining scalable, maintainable, secure, and supportable.

Requires strong industry experience building production software, with demonstrated hands-on experience developing and deploying applications using LLMs, Generative AI, or Agentic AI technologies.

Technical Skills

Software Development: Strong proficiency in Python and experience developing production-grade back-end applications and APIs. Experience with SQL and modern software engineering practices is required.

Generative AI & LLMs: Strong hands-on industry experience integrating LLMs into production applications, including prompt design, structured outputs, context management, model selection, and API-based model integration.

Agentic AI: Experience designing AI agents and agentic workflows involving tool use, multi-step reasoning, orchestration, state management, and interaction with enterprise systems and APIs.

Retrieval & Grounding: Experience implementing grounding and retrieval patterns such as Retrieval-Augmented Generation (RAG), embeddings, vector search, and enterprise knowledge retrieval where appropriate.

AI Evaluation: Experience developing evaluation frameworks for AI applications, including measuring response quality, reliability, groundedness, task completion, latency, and other relevant product metrics.

AI Engineering: Strong understanding of the challenges associated with production AI systems, including non-deterministic outputs, hallucinations, context management, failure handling, observability, model/version changes, and cost management.

Cloud & Deployment: Experience deploying AI-enabled applications in cloud environments, preferably GCP, and working with containerized environments such as Docker and Kubernetes.

API & Integration Design: Strong experience designing and consuming RESTful APIs and integrating AI capabilities into existing distributed applications and enterprise systems.

Software Craftsmanship: Strong understanding of clean-code principles, automated testing, CI/CD, Git workflows, observability, and maintainable software architecture.

Enterprise AI Platforms: Experience integrating applications with shared enterprise AI platforms, model gateways, or similar centralized AI capabilities is highly desirable.

Professional Skills

Technical Leadership & Enablement: Ability to provide hands-on technical leadership while deliberately transferring knowledge and capability to the existing development team.

Teaching & Mentorship: Strong ability to explain emerging AI concepts to experienced software developers and translate those concepts into practical engineering patterns, examples, and working solutions.

Product Thinking: Ability to evaluate AI opportunities based on user value, feasibility, reliability, and business impact rather than applying AI solely because the technology is available.

Problem Solving & Experimentation: Comfortable operating in an emerging technical space where requirements and implementation patterns may initially be ambiguous. Able to rapidly prototype, evaluate results, and turn successful experiments into maintainable production solutions.

Cross-Team Collaboration: Proven ability to work across product, engineering, analytics, architecture, and enterprise platform teams and establish alignment around technical approaches and responsibilities.

Communication: Ability to clearly communicate AI concepts, solution designs, limitations, risks, and technical tradeoffs to both technical and non-technical stakeholders.

Platform Mindset: Prioritizes reusable enterprise capabilities and established platform patterns over unnecessary custom infrastructure, while identifying and communicating gaps where platform capabilities do not meet product needs.

Responsible AI Development: Applies appropriate safeguards, testing, monitoring, access controls, and human oversight when introducing AI capabilities into business-critical workflows.

Continuous Learning: Maintains strong awareness of rapidly evolving LLM and Agentic AI technologies and translates relevant developments into practical recommendations for the team

Key Skills

Education

Not specified