Machine Learning Engineer

Job Summary

Chart the Course for the Future of Flight. This role builds Machine Learning tools to enable reduction of the engineering workforce burden by developing internal ML-based tools across the organisation. This person will develop, research, and deploy ML algorithms across different engineering disciplines.

Roles and Responsibilities

• Research, Develop, and implement machine learning based tools across LLMs, Multimodal systems, Generative AI systems
• Develop training pipelines, architecture, and prototyping for ML algorithms
• Work on productising research prototypes
• Conduct experiments to benchmark new techniques and evaluate model behavior
• Develop systematic evaluation methodology: test sets, accuracy metrics, citation quality scoring, false positive/negative analysis
• Deploy AI tools to engineering teams with structured pilots, baseline measurement, and documented adoption outcomes

Required Qualifications

• 2+ years in developing ML systems with focus on LLM-based products
• Proven understanding of fundamental data structures and the ability to apply them to solve complex problems
• Experience with PyTorch, TensorFlow
• GenAI engineering including concepts relating to scaffolding, harnessing, and RAG pipelines
• Development experience with retrieval pipeline skills (vector databases, chunking strategies, re-ranking, citation grounding)
• Experience with Containerization (Docker/Kubernetes)

Preferred Qualifications

• Understanding and deployment of Reinforcement Learning based tools
• Understanding of mathematics, particularly linear algebra and probability theory
• Experience with distributed processing frameworks and ElasticSearch
• Prior knowledge of multimodal systems and VLM systems
• Experimentation and deployment of Local GenAI model deployment using tools like Olama
• Experience building multi-step agentic workflows

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