Role overview
Join our AI Teams and help us build production-grade AI solutions that directly support and scale our global sales organization within the airline industry!
You will play a critical role in increasing sales efficiency, response quality, and speed-to-market by embedding AI into sales and pre-sales processes. Your work will focus on automating and augmenting sales-related workflows such as RFP/RFI processing, proposal generation, customer-specific solution descriptions, pricing inputs, and knowledge retrieval.
This role combines hands-on technical leadership with the opportunity to shape and grow a distributed AI engineering team. You will act as a technical lead for sales-focused AI initiatives and, over time, take responsibility for guiding and mentoring a team of AI engineers.
What you'll work on
- Architect and implement scalable AI/ML solutions on Azure (preferred) or GCP, leveraging services such as Azure Databricks, Azure ML Studio, Data Lake Storage, Event Hubs and Functions (or GCP equivalents)
- Design and build AI solutions for sales use cases such as:
- Automated RFP/RFI ingestion, classification, and response generation
- Intelligent document understanding (PDFs, Word, Excel, presentations)
- Knowledge retrieval across product documentation, contracts, and past proposals
- Sales assistant capabilities powered by LLMs
- Develop and deploy LLM-based systems using prompt engineering, fine-tuning, and retrieval-augmented generation (RAG)
- Integrate ML and GenAI solutions with enterprise platforms such as CRM systems, document repositories, collaboration tools, and workflow engines
- Drive the evolution of successful PoCs into robust MVPs ready for adoption by sales and pre-sales teams
- Implement MLOps practices including CI/CD for ML, infrastructure-as-code, model versioning, and automated retraining and deployment pipelines
- Provide technical guidance, code reviews, and architectural support to AI engineers, with a growing responsibility for mentoring and leading a distributed team
- Stay ahead of emerging AI trends, tools, and frameworks to continuously push innovation boundaries
What we're looking for
- Hands-on experience with document AI, NLP, semantic search, or knowledge graph solutions
- Experience with CI/CD pipelines for ML (Azure DevOps, GitHub Actions) and infrastructure-as-code (Terraform or ARM templates)
- Exposure to MLOps practices and tools (e.g. CI/CD, model versioning, MLflow, Kubeflow)
- Hands-on Spark/Databricks expertise
- Experience building PoCs and MVPs in AI/ML contexts
- Familiarity with data engineering fundamentals for preparing and curating datasets for experimentation and model training
- Demonstrated experience acting as a technical lead, owning architecture decisions and guiding other engineers