Machine Learning Engineer Iv

Year    MH, IN, India

Job Description

Overview:

Who We Are:


Ever wonder who brings the entertainment to your flights? Panasonic Avionics Corporation is #1 in the industry for delivering inflight products such as movies, games, WiFi, and now Bluetooth headphone connectivity!


How exciting would it be to be a part of the innovation that goes into creating technology that delights millions of people in an industry that's here to stay! With our company's history spanning over 40 years, you will have stability, career growth opportunities, and will work with the brightest minds in the industry. And we are committed to a diverse and inclusive culture that will help our organization thrive! We seek diversity in many areas such as background, culture, gender, ways of thinking, skills and more.


If you want to learn more about us visit us at www.panasonic.aero. And for a full listing of open job opportunities go to www.panasonic.aero/join-us/.



The Position:

We're seeking an experienced

Machine Learning Engineer 4

to lead advanced AI initiatives in the

In-Flight Entertainment & Connectivity (IFEC)

domain. This role focuses on designing and scaling cutting-edge ML solutions that enhance personalization and efficiency in passenger experiences. You'll drive innovation through GenAI, Retrieval-Augmented Generation (RAG), and scalable ML systems, while mentoring a high-performing team of engineers.


Responsibilities:

Key Responsibilities:

Technical Leadership:


Lead the design and development of robust, scalable ML architectures tailored for IFEC applications. Own end-to-end system design, from data ingestion to real-time inference.

ML & GenAI Development:


Build, fine-tune, and deploy machine learning models, with a strong focus on

Generative AI

and

RAG pipelines

using frameworks like

TensorFlow

,

PyTorch

, and

LangChain

.

Infrastructure & Deployment:


Develop and manage cloud-native ML pipelines (AWS/GCP/Azure), CI/CD workflows, and containerized deployments using Docker/Kubernetes.

Research & Innovation:


Stay at the forefront of ML/GenAI advancements. Evaluate new technologies, publish white papers, and contribute to R&D initiatives.

Collaboration & Mentorship:


Work cross-functionally with data scientists, backend engineers, and product stakeholders. Mentor junior ML engineers to raise team capability.

Performance Monitoring & Optimization:


Continuously monitor, evaluate, and refine models to improve prediction accuracy, latency, and scalability.



Requirements:


+ 8 -12 years of hands-on experience in

ML engineering and software development

, ideally in aerospace, telecom, or tech-driven industries.
+ Proven expertise in

Generative AI

,

RAG (Retrieval-Augmented Generation)

systems,

LLMs (e.g., GPT, LLaMA)

, and vector databases like

FAISS

,

Pinecone

, or

Weaviate

.
+ Proficient in

Python

and

Java

, with in-depth knowledge of

TensorFlow

,

PyTorch

, and ML lifecycle tools (MLflow, Airflow).
+ Strong knowledge of cloud services (AWS/GCP/Azure) and MLOps best practices.
+ Solid understanding of data structures, algorithms, and distributed systems.
+ Excellent problem-solving, leadership, and communication skills



Qualifications:

Bachelor's or Master's degree in

Computer Science

,

Machine Learning

,

Data Science

, or a related technical field.



Our Principles:


Contribution to Society | Fairness & Honesty | Cooperation & Team Spirit | Untiring Effort for Improvement | Courtesy & Humility | Adaptability | Gratitude



What We Offer:


At Panasonic Avionics Corporation we realize the most important aspects in leading our industry are the bright minds behind everything we do. We are proud to offer our employees a highly competitive, comprehensive and flexible benefits program.

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Job Detail

  • Job Id
    JD3720744
  • Industry
    Not mentioned
  • Total Positions
    1
  • Job Type:
    Contract
  • Salary:
    Not mentioned
  • Employment Status
    Permanent
  • Job Location
    MH, IN, India
  • Education
    Not mentioned
  • Experience
    Year