Hiring.Camp

AI / ML Engineer

Ssctech

·

Today

Location
Hyderabad India - Block B · Pune India - Commerzone Building 7 · Mumbai, India · WeWork Bengaluru
Type
Full-time
Department
Engineering
Closing date
Today
Source
Workday

Description

SS&C is a leading provider of mission-critical, AI-powered technology and services empowering financial services and healthcare organizations to work smarter, faster, and securely. Founded in 1986, SS&C is headquartered in Windsor, Connecticut, and has offices worldwide. More than 23,000 financial services and healthcare organizations, from the world's largest companies to small and mid-market firms, rely on SS&C for expertise, scale, and technology.


Job Description


  • 4 – 7 years of hands-on experience in Machine Learning, Natural Language Processing, and Deep Learning model development.

  • Fluency in Python and main ML packages: PyTorch, Hugging Face Transformers, Scikit-Learn, Pandas, and NumPy.

  • Practical experience fine-tuning transformer-based architectures on proprietary enterprise domain data.

  • Solid working knowledge of document OCR processing tools (LayoutLM, Tesseract, PaddleOCR) and PDF parsing frameworks.

  • Rigorous understanding of model evaluation metrics, statistical sampling, and error analysis.

 

 

Unless explicitly requested or approached by SS&C Technologies, Inc. or any of its affiliated companies, the company will not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services.

 

 

 

 

 

 

 

 

 

 

Applications will be accepted on an ongoing basis until the position is filled.

 

 

SS&C Technologies is an Equal Employment Opportunity employer and does not discriminate against any applicant for employment or employee on the basis of race, color, religious creed, gender, age, marital status, sexual orientation, national origin, disability, veteran status or any other classification protected by applicable discrimination laws.

Skills

PythonMachine LearningDeep LearningPyTorchPandasNumPyScikit-learn

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