Full Stack Developer (Expert) (AI Engineering + Knowledge of LLMs and Generative AI Application engineering + Retrieval-Augmented Generation (RAG) + Python/Java + APIs + Docker/Kubernetes/Terraform)

Introduction

We are seeking an experienced Expert AI Engineer to design, develop and operate reusable AI platform capabilities and production-grade Generative AI solutions within an enterprise environment.

The successful candidate will have expert-level Python engineering skills, hands-on experience building and operating Large Language Model (LLM) applications and AI agents, strong knowledge of Retrieval-Augmented Generation (RAG), practical experience with Snowflake and enterprise data engineering, and the ability to integrate AI into the software development lifecycle.

This role focuses on developing reusable AI capabilities, enabling teams to move AI use cases from experimentation into secure, scalable and production-ready solutions, and establishing engineering standards that improve developer productivity, software quality, reliability and operational efficiency.

Key Responsibilities

• Design and implement reusable AI platform services, APIs, libraries, templates and engineering patterns.

• Establish common AI capabilities that enable multiple development teams to implement use cases consistently.

• Work with use-case teams from discovery and prototyping through to production implementation.

• Transform successful AI experiments into secure, scalable, reusable and production-ready solutions.

• Build and operate AI agents, LLM applications and Retrieval-Augmented Generation (RAG) capabilities.

• Develop prompts, context packages, tool integrations, orchestration mechanisms and evaluation processes.

• Implement structured outputs, tool calling, AI guardrails and human approval controls for high-impact actions.

• Integrate AI agents and services with enterprise tools, APIs, applications and data platforms.

• Embed AI into specification, development, testing, code review, documentation and operational workflows.

• Develop reusable AI-enabled developer tools, engineering accelerators and workflows that improve productivity while maintaining engineering accountability.

• Engineer Snowflake data structures, data models and integration patterns required by enterprise products and AI use cases.

• Integrate Snowflake with platform services, data pipelines, APIs, AI capabilities and enterprise applications.

• Implement AI evaluation, monitoring, observability, security and production-readiness practices.

• Ensure AI solutions can be operated, monitored, diagnosed and continuously improved.

• Apply enterprise security, privacy, governance, information protection, auditability, resilience and operational support standards.

• Identify recurring requirements across use cases and develop shared, reusable platform capabilities rather than isolated solutions.

• Lead technical architecture and design discussions.

• Conduct code reviews and technical design assessments.

• Mentor engineers and establish consistent engineering standards.

• Support production deployment, monitoring, incident resolution and continuous improvement.

• Use telemetry and operational feedback to improve performance, reliability, developer experience and cost efficiency.

Essential Skills

• Expert knowledge of Large Language Models (LLMs) and Generative AI application engineering.

• Hands-on experience building and operating production-grade LLM applications and AI agents.

• Strong understanding of Retrieval-Augmented Generation (RAG).

• Prompt engineering and context engineering.

• Agent and tool integration and orchestration.

• Structured outputs and tool calling.

• Model and application evaluation.

• AI guardrails and human approval mechanisms.

• AI observability, reliability and production monitoring.

• Responsible and secure AI engineering.

• Strong practical capability using AI throughout the software engineering lifecycle.

• Specification-driven development.

• AI-assisted implementation and refactoring.

• AI-assisted test generation and quality engineering.

• AI-supported code review and documentation.

• Ability to build reusable AI-enabled developer tooling and workflows.

• Understanding that AI-generated outputs require accountable engineering review for correctness, quality and security.

• Expert Python engineering.

• Strong API and microservice engineering.

• REST-based service integration.

• Event-driven and asynchronous architecture.

• Containers and Kubernetes.

• CI/CD and automated testing.

• Infrastructure as Code.

• Secure software development.

• Production observability and operational readiness.

• Strong architecture and systems-thinking capability.

• Strong Snowflake engineering and implementation capability.

• Design and implementation of data models suitable for AI and application consumption.

• Advanced SQL.

• Data ingestion and transformation.

• ETL/ELT engineering.

• Metadata management, data lineage and data quality.

• Secure data access patterns.

• Integration between Snowflake, AI services, APIs and enterprise applications.

• Performance- and cost-conscious data engineering.

Advantageous Skills and Certifications

• Snowflake certifications or demonstrable enterprise Snowflake implementation experience.

• Experience with modern AI/LLM frameworks and model APIs.

• Knowledge of embeddings, vector retrieval and semantic search.

• Knowledge graphs, ontologies and semantic modelling.

• Apache Spark and Kafka.

• AWS cloud engineering.

• Docker, Kubernetes and Terraform.

• GitHub Enterprise and GitHub Actions.

• Experience building internal developer platforms or reusable engineering tooling.

• Enterprise data-product and federated-data architecture experience.

• Observability, Site Reliability Engineering (SRE) and resilience engineering.

• Responsible AI, privacy, information protection and data governance.

• Automotive, mobility or manufacturing industry experience.

• Snowflake certification.

• AWS certification.

• Kubernetes or Cloud Native certification.

• Terraform certification.

• AI or Machine Learning certification.

• Data Engineering or Architecture certification.

Qualifications and Experience

• Degree or diploma in Computer Science, Software Engineering, Artificial Intelligence, Machine Learning, Data Engineering, Information Technology or a related discipline.

• Typically 7+ years of relevant engineering experience.

• Demonstrable experience building and operating complex production systems.

• Expert-level Python engineering capability.

• Demonstrable production experience with Generative AI and LLM solutions.

• Strong data and integration engineering background.

• Practical experience implementing AI-assisted or AI-native engineering practices.

• Experience with Snowflake implementation or comparable enterprise cloud data platforms.

• Evidence of taking AI solutions beyond prototypes into governed, secure and production-ready implementations.