FIND_THE_RIGHTJOB.
India
EXL/LAM/1505396
Number Of Positions
1
Band
B2
Band Name
Lead Assistant Manager
Cost Code
D014267
Campus/Non Campus
NON CAMPUS
Employment Type
Permanent
Requisition Type
New
Max CTC
1000000.0000 - 1500000.0000
Complexity Level
Not Applicable
Work Type
Hybrid – Working Partly From Home And Partly From Office
Group
Analytics
Sub Group
Analytics - UK & Europe
Organization
Services
LOB
Analytics - UK & Europe
SBU
Analytics
Country
India
City
Gurgaon
Center
EXL - Gurgaon Center 38
Job Summary:
We are looking for a motivated and skilled Machine Learning Engineer to join our team. The ideal candidate will have hands-on experience in building, deploying, and maintaining machine learning models and pipelines. You will collaborate closely with data scientists, software engineers, and product teams to turn data insights into scalable, production-ready solutions.
Key Responsibilities:
Design, develop, and deploy machine learning models and algorithms for real-world applications.
Collaborate with data scientists to understand model requirements and translate prototypes into production code.
Preprocess, clean, and analyze large datasets to improve model performance and accuracy.
Optimize and fine-tune models using hyperparameter tuning, feature engineering, and cross-validation.
Build and maintain scalable data pipelines and automated workflows for model training and deployment.
Monitor and troubleshoot deployed models, ensuring reliability and performance in production environments.
Work with engineering teams to integrate ML solutions into existing products and services.
Stay updated with the latest advancements in machine learning and apply best practices.
Qualifications:
Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field.
2-5 years of experience in machine learning or data science roles.
Strong programming skills in Python and experience with ML libraries such as TensorFlow, PyTorch, scikit-learn, or similar.
Solid understanding of machine learning concepts, algorithms, and statistical methods.
Experience with data processing tools and frameworks (e.g., Pandas, NumPy, Spark).
Familiarity with cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes) is a plus.
Knowledge of software engineering best practices, version control (Git), and CI/CD pipelines.
Strong problem-solving skills and the ability to work both independently and collaboratively.
Workflow Type
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