About Job/Role

We are seeking an experienced Machine Learning Engineer to lead end-to-end ML initiatives that enhance In-Flight Entertainment & Connectivity (IFEC) systems.
This role involves technical leadership, hands-on ML model development, and mentoring, with a focus on innovation, personalization, and GenAI-driven solutions for next-generation passenger experiences.

Roles and Responsibilities

Technical Leadership:
- Architect, design, and implement scalable ML systems for optimized content delivery, connectivity, and personalization.
- Lead decision-making for ML infrastructure, cloud pipelines, and deployment strategies.
ML Model Development:
- Build and deploy predictive and generative models using TensorFlow, PyTorch, and other frameworks
- Apply GenAI techniques (LLMs, RAG-based retrieval, embeddings, fine-tuning) to deliver personalized in-flight experiences.
Infrastructure & Deployment:
- Develop cloud-native ML pipelines (AWS/Azure/GCP) with CI/CD integration.
- Ensure scalable, secure, and efficient deployment of ML solutions
Research & Innovation:
- Stay at the forefront of ML and GenAI advancements, evaluating applicability to IFEC use cases.
- Contribute to white papers, PoCs, and technical publications.
Collaboration & Mentorship:
- Partner with data scientists, engineers, and product managers to translate business needs into ML-driven solutions.
- Mentor and guide junior engineers in ML best practices and GenAI applications.
Performance Optimization:
- Continuously monitor and refine ML models for latency, scalability, and accuracy improvements.
- Leverage observability and monitoring tools for ongoing optimization.

Required Qualification

Experience: 5-8 years in ML engineering and software development (aerospace/tech industry preferred).
Programming: Strong in Python and Java
ML Frameworks: Hands-on with TensorFlow, PyTorch, Hugging Face Transformers.
GenAI Expertise: Proven experience in LLMs, RAG (Retrieval-Augmented Generation), embeddings, fine-tuning, and prompt engineering.
Cloud & Deployment: Proficiency in AWS, Azure, or GCP for ML model lifecycle management.
Problem-Solving: Strong analytical, architectural, and technical documentation skills
Education: Bachelor’s/Master’s degree in Computer Science, Data Science, or related fields.

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