SR. Python (AIML And GenAI)

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