Senior AI/ML Solutions Engineer

5 days ago


Bengaluru, Karnataka, India BYTESPRINT TALENT SEARCH LLP Full time

Job Description:

We are seeking a highly skilled Senior AI/ML Engineer to lead our team in Bengaluru at MG Road Metro Station. This hands-on role is critical in building and autonomizing AI/ML platforms with a focus on integrating solutions and implementing backend pipelines.

The ideal candidate will have extensive experience in deploying deep learning models in production environments and be capable of contributing to multiple client initiatives.

Key Responsibilities:

Platform Development: Lead the design and implementation of AI/ML platforms, focusing on automation of data pipelines, coding standards, and integration with full-stack applications.

AI/ML Integration: Integrate advanced AI/ML solutions into client platforms, particularly those in the healthcare and life sciences sectors.

Model Deployment: Build, deploy, and manage NLP systems and language models, ensuring they meet enterprise standards for performance and scalability.

Collaboration: Work closely with cross-functional teams to implement features, improve models, and deliver on project goals.

Continuous Improvement: Proactively identify opportunities to enhance platform performance, model accuracy, and the overall AI/ML experience.

Technical & Industry Competencies:

Python Expertise: Strong hands-on coding skills in Python, with a focus on AI/ML applications.

Deep Learning Deployment: Proven experience deploying deep learning models in production, with a strong understanding of Transformer models.

NLP Experience: At least 4+ years of experience building and deploying NLP systems, preferably within the healthcare or life sciences industry.

ML Pipeline Implementation: Extensive experience in developing and deploying ML pipelines, including data preprocessing, model training, validation, deployment, and integration.

APIs & Frameworks: Proficient in using frameworks like Django, Flask for packaging NLP systems as APIs.

Advanced Training Techniques: Expertise in deep learning training techniques such as few-shot learning, zero-shot learning, transfer learning, and federated learning.


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