SR. Python (AIML and GenAI)
Cognizant
Date: 7 hours ago
City: Chennai, Tamil Nadu
Contract type: Full time
Python with (AI, ML and GENAI)
Skillset : Python, Agentic AI, Artificial Intelligence and Machine Learning (AIML) combined with Generative AI (GenAI)
Must have Gen AI experience minimum 2+yrs of experience.
The resource should have prior experience leveraging Copilot or Claude for code generation..
Implements distributed ML experimentation and training platform for firm-wide use in accordance with the requirements and architecture.
Implements and supports tools and workflows to facilitate machine learning experiments, automated training runs, and production deployments.
Extends machine learning libraries and frameworks to support complex experimentation, training, and serving requirements.
Delivers thoughtful data scientist experience with APIs and SDKs for the firm-wide machine learning platform.
Collaborates with infrastructure engineering, product management, and security and compliance teams to deliver tailored, robust solutions.
Required Qualifications, Capabilities, And Skills
Formal training or certification on machine learning concepts and 3+ years applied experience.
Hands-on practical experience in system design, application development, testing, and operational stability
Overall knowledge of the Python Software Development Life Cycle
Programming skills in Python and experience with ML frameworks and libraries such as Ray, TensorFlow, PyTorch, Scikit-Learn, etc.
Knowledge of model development processes and lifecycle in an ML environment.
Hands-on experience with public cloud technologies, especially with AWS, in the context of ML engineering workflows - featurization, experimentation, training, evaluation, deployment, serving, and monitoring.
Preferred Qualifications, Capabilities, And Skills
Knowledge of Big Data and related technologies such as Hadoop, Spark, and Airflow.
Knowledge of SageMaker, EMR, and AWS ML stack.
Knowledge of Kubernetes ecosystem, including EKS, Helm Charts, and Custom Operators.
Strong experience implementing DevOps practices using tools such as Docker, Jenkins, Spinnaker, and Terraform
Skillset : Python, Agentic AI, Artificial Intelligence and Machine Learning (AIML) combined with Generative AI (GenAI)
Must have Gen AI experience minimum 2+yrs of experience.
The resource should have prior experience leveraging Copilot or Claude for code generation..
Implements distributed ML experimentation and training platform for firm-wide use in accordance with the requirements and architecture.
Implements and supports tools and workflows to facilitate machine learning experiments, automated training runs, and production deployments.
Extends machine learning libraries and frameworks to support complex experimentation, training, and serving requirements.
Delivers thoughtful data scientist experience with APIs and SDKs for the firm-wide machine learning platform.
Collaborates with infrastructure engineering, product management, and security and compliance teams to deliver tailored, robust solutions.
Required Qualifications, Capabilities, And Skills
Formal training or certification on machine learning concepts and 3+ years applied experience.
Hands-on practical experience in system design, application development, testing, and operational stability
Overall knowledge of the Python Software Development Life Cycle
Programming skills in Python and experience with ML frameworks and libraries such as Ray, TensorFlow, PyTorch, Scikit-Learn, etc.
Knowledge of model development processes and lifecycle in an ML environment.
Hands-on experience with public cloud technologies, especially with AWS, in the context of ML engineering workflows - featurization, experimentation, training, evaluation, deployment, serving, and monitoring.
Preferred Qualifications, Capabilities, And Skills
Knowledge of Big Data and related technologies such as Hadoop, Spark, and Airflow.
Knowledge of SageMaker, EMR, and AWS ML stack.
Knowledge of Kubernetes ecosystem, including EKS, Helm Charts, and Custom Operators.
Strong experience implementing DevOps practices using tools such as Docker, Jenkins, Spinnaker, and Terraform
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