JPMorgan Applied AI ML Lead - MLOps 

India, Telangana, Hyderabad 

486683110

Job Responsibilities:

• Deploy and maintain infrastructure for providing an effective model development platform for data scientists and ML engineers that integrates with enterprise data ecosystem
• Build, deploy and maintain ingress/egress and feature generation pipelines to calculate input features for model training and inference
• Deploy and maintain infrastructure for batch and real-time model serving, in high throughput, low latency applications, at scale
• Identify, deploy and maintain high quality model monitoring and observability tools
• Deploy and maintain infrastructure for compute intensive tasks such as hyper-parameter tuning and interpretability and explain ability
• Partners with product, architecture, and other engineering teams to define scalable and performant technical solutions
• Leverages deep technical expertise to design extensible and scalable solutions, and to coach and grow individuals and teams
• Ensures team executes work according to compliance standards, SLAs, and business requirements, to meet the objectives of an initiative. Anticipates the needs of broader teams and potential dependencies with other teams
• Identifies and mitigates issues to execute a book of work while escalating issues as necessary
• Proactively helps maintain high operational excellence standards for our production systems. Encourages development of technological methods and techniques within team
• Creates a culture of diversity, equity, inclusion, and respect for team members and prioritizes diverse representation

Required qualifications, capabilities and skills:

• Formal training or certification on software engineering concepts and 5+ years applied experience
• Deep experience and passion in model training, build, deployment and execution ecosystem such as Sagemaker and/or Vertex AI
• Experience in monitoring and observability tools to monitor model input/output and features stats
• Operational experience in big data tools such as Spark, EMR, Ray
• Experience and interest in ML model architectures - linear/logistic regression, Gradient Boosted Trees, Neural Network architectures
• Solid grounding in engineering fundamentals and analytical mindset
• Experience in taking initiative and embracing iterative development

Preferred qualifications, capabilities and skills :

• Experience with recommendation and personalization systems
• Programming languages: Python, some Java
• Experience in containers (docker ecosystem), container orchestration systems (Kubernetes, ECS), DAG orchestration (Airflow, Kubeflow etc.)
• Experience with cloud technologies—EC2, Sagemaker, IAM