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Experiment to evaluate application performance, including designing experiments to evaluate the performance of machine learning models, and then analyze and interpret the results to improve model performance.
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Gurugram, IND
Noida, IND
Gurugram, IND
Gurugram, IND
Gurugram, IND
Noida, IND
Gurugram, IND
Gurugram, IND
Gurugram, IND
Work with stakeholders to develop and tune algorithms to address business needs. You should be able to effectively communicate your ideas in non-technical terms to help educate business partners
Evaluate, fine‑tune, and deploy LLMs and foundation models; implement RAG, guardrails, and prompt engineering best practices (conversant with Github Copilot).
Conversant with LLM security practices – Secure model deployment, Input validation, Data verification (guardrails) etc.
Write production quality Python code for: Feature Engineering Model evaluation Interface services (APIs)
Feature Engineering
Model evaluation
Interface services (APIs)
Conversant with Spring AI – building MCPs, tools, pre-plugin and post-plugin etc.
Build high performant Ingestion pipelines.
Expertise across cloud (AWS), data engineering, ML/GenAI and data ingestion techniques
Work with CI/CD pipelines (Github Actions, Gitlab, Jenkins)
Mentor engineers on ML best practices (without direct people management)
Collaborate on a high-performing, agile team with a global presence
Overall, 8 -10 years of experience into Data Engineering, Ingestion, AI ML / Gen AI related projects.
Specifically, AI / Gen AI / ML project(s) with 5+ years of experience
Master’s degree with preferred concentrations in Computer Science, Data Science, Math, Actuarial Science, Engineering, or related field.
Experience writing in Python or Spark (ScalaSpark or PySpark) and with popular machine learning libraries such as TensorFlow, Keras, PyTorch, and conversant with JAVA (Spring AI)
Conversant with Spring boot services
AI/ML architects should have experience with data preparation and data engineering tasks such as data cleaning, feature engineering, and data transformation
Knowledge of deep learning architectures and techniques, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and reinforcement learning.
Familiarity with AWS data and data science tools including SageMaker, Glue, Lambdas, etc.
Experience in Agile and DevOps development process
Must be able to clearly communicate complex technical concepts to a non-technical audience
Verified company details for this employer are not available yet.
Full-time
Senior · 8+ years experience
Remote
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