What are the responsibilities and job description for the Senior Engineer, AI Platforms position at Working at Signature Aviation?
At Signature Aviation, we are modernizing operations and customer experiences through technology and AI. We are seeking a Senior Engineer – AI Platforms to help design, build, and implement AI-driven solutions that support intelligent automation, predictive insights, and AI-enabled workflows. In this role, you will contribute to the development of LLM-based applications and AI integrations across enterprise platforms, partnering closely with architects, product teams, and data teams to deliver secure, scalable, and responsible AI solutions.
Qualifications:
Minimum Education and/or Experience:
- Bachelor’s degree in Computer Science, Engineering, or a related field.
- 5 years of professional experience in software engineering, AI/ML, or data-driven platforms.
Additional knowledge and skills:
- Hands-on experience building and deploying LLM-powered applications in production or near-production environments.
- Working knowledge of RAG pipelines, embeddings, and vector search technologies.
- Experience developing cloud-based AI solutions (Azure preferred).
- Strong software engineering fundamentals and ability to deliver production-quality code.
- Familiarity with responsible AI concepts, data privacy, and secure system design.
- Exposure to regulated or operationally complex environments is a plus.
Tech Stack & Skill Requirements:
- AI/ML Frameworks: LangChain, LlamaIndex, Semantic Kernel, Hugging Face (or similar).
- LLMs: OpenAI GPT, Anthropic Claude, LLaMA, Falcon, or comparable models.
- Vector Databases: Pinecone, Weaviate, Redis Vector, Azure Cognitive Search.
- Programming Languages: Python (preferred); TypeScript/Node.js for integrations.
- Cloud Platforms: Azure AI/ML services, Azure OpenAI, AWS Bedrock, or GCP Vertex AI.
- MLOps & Monitoring: MLflow, Azure Machine Learning, Langfuse, or similar tools.
- Security & Compliance: Familiarity with OAuth 2.0, RBAC, and data privacy principles.
- Security: OAuth 2.0, RBAC, data privacy, compliance frameworks (GDPR, PCI).
Responsibilities:
- Evaluate AI Technologies: Support the assessment and proof-of-concept evaluation of AI platforms, LLMs, and agent frameworks for enterprise use.
- Develop AI Agents & Workflows: Build and enhance AI agents capable of supporting multi-step reasoning, automation, and task execution under established architectural patterns.
- Engineer LLM Applications: Design and implement LLM-based applications, including retrieval-augmented generation (RAG) pipelines, prompt engineering, and model optimization.
- Integrate with Enterprise Platforms: Contribute to embedding AI capabilities into OMS, CRM, loyalty, and digital platforms in partnership with engineering and product teams.
- Apply AI Engineering Standards: Follow established practices for AI model deployment, monitoring, safety, and compliance.
- Cross-Functional Collaboration: Work closely with Product, Digital Engineering, and Data Science teams to deliver AI-powered features and enhancements.
- Responsible AI & Security: Adhere to AI governance, privacy, and security controls when developing and deploying AI solutions.
- Mentorship & Knowledge Sharing: Provide informal mentorship to junior engineers and share best practices, while remaining primarily focused on hands-on delivery.