Live opening · Posted 5 days ago

AI/ML Engineer

SAAP Technologies · Hyderabad, Telangana, India (On-site)
Linkedin No
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At a glance

The key details from the original listing.

Posted 5 days ago
CompanySAAP Technologies
LocationHyderabad, Telangana, India (On-site)
Work modeNo
SourceLinkedin
Listed5 days ago

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About the role

Description supplied by the original job listing.

Company Description : SAAP Technologies delivers secure, scalable, and innovative technology solutions in AI, Salesforce, Product Development, and IT Staffing. The company operates across the U.S., UK, and India, providing services to a diverse range of clients and industries. SAAP Technologies focuses on leveraging advanced technologies to solve complex business problems and drive digital transformation. Team members collaborate across global locations, contributing to projects that emphasize reliability, performance, and customer success.
Department: Digital / IT — Healthcare Applications
Location: Hyderabad
Experience Level: 4–8 years
Role Summary
We are looking for an AI/ML Engineer to build and integrate AI/ML capabilities — such as speech-to-text and predictive analytics — into healthcare applications.
Key Responsibilities
Design, train, and evaluate ML models for defined use cases (e.g., speech-to-text, analytics)
Build data pipelines to support model training and inference
Integrate ML models into application workflows via APIs / services
Monitor model performance in production and support retraining as needed
Collaborate with Product Managers and clinical SMEs to define use cases and success criteria
Technical Skills Required
Python, with TensorFlow / PyTorch
NLP techniques and libraries
Azure ML (or equivalent cloud ML services)
Basic MLOps practices (model versioning, monitoring)
Experience
4–8 years in AI/ML engineering or data science, ideally with some healthcare or enterprise application exposure
Behavioural Competencies
Strong analytical and experimentation mindset
Ability to translate ambiguous problems into ML solutions
Collaborative approach with product and engineering teams
Attention to model accuracy, bias, and responsible AI practices

Work arrangement
No

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