JPMorgan Applied AI ML Lead - Engineering 

India, Karnataka, Bengaluru 

617438597

Job responsibilities

• Design and implement end-to-end machine learning solutions for the production environment to solve complex problems related to personalized financial services in retail and digital banking.
• Work closely with other Machine Learning practitioners and cross-functional teams to translate business requirements into technical solutions and drive innovation in our banking products and services.
• Collaborate with Machine Learning engineers, product managers, key business stakeholders, engineering, and platform partners to deploy projects that deliver cutting-edge machine learning-driven digital solutions.
• Write code to create several machine learning experimentation pipelines.
• Design and implement feature engineering pipelines and push them to feature stores.
• Collaborate with data engineers and product analysts to preprocess and analyze large datasets from multiple sources.
• Execute experiments and validations at scale, and review results with Lead and Products.
• Create model serving pipelines that meet consumption SLAs.
• Write production-grade code for both training and inference functions.
• Collaborate with MLOps engineers in developing and testing the training and inference applications under the production architecture blueprint, often in integration with upstream and downstream applications.
• Collaborate with MLOps engineers to register the models' artifacts, maintain code repositories, and prepare for CI/CD execution and post-production monitoring setups.
• Drive end-to-end system architecture in collaboration with ML, MLOps, and Architecture leads.
• Communicate and collaborate with Platform and Engineering partners to bring in the latest advancements to improve the scale, consistency, reliability, and trustworthiness of the ML solutions.
• Mentor Junior Machine Learning associates in delivering successful projects and building successful careers in the firm.
• Participate and contribute back to firm-wide Machine Learning communities through patenting, publications, and speaking engagements

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience
• BS, MS or PhD degree in Computer Science, Statistics, Mathematics or Machine learning related field.
• Expert proficiency in implementing ML models at least one of the following areas: Natural Language Processing, Knowledge Graph, Computer Vision, Speech Recognition, Reinforcement Learning, Ranking and Recommendation, or Time Series Analysis.
• Foundational knowledge in Data structures, Algorithms, Machine Learning, Data Mining, Information Retrieval, Statistics.
• Demonstrated expertise in machine learning frameworks: Tensorflow, Pytorch, pyG, Keras, MXNet, Scikit-Learn.
• Expert programming knowledge of python, spark; Expert coding knowledge on vector operations using numpy, scipy;
• Coding knowledge on distributed computation using Multithreading, Multi GPUs, Dask, Ray, Polars etc.
• Strong analytical and critical thinking skills for problem solving.
• Excellent written and oral communication along with demonstrated teamwork skills.
• Demonstrated ability to clearly communicate complex technical concepts to both technical and non-technical audiences.
• Experience in working with interdisciplinary teams and collaborating with other researchers, engineers, and stakeholders.
• A strong desire to stay updated with the latest advancements in the field and continuously improve one's skills

• Experience with distributed data/feature engineering using popular cloud services like AWS EMR
• Experience with large scale training, validation and testing experiments
• Experience with cloud Machine Learning services in AWS i.e. Sagemaker
• Experience with Container technology like Docker, ECS etc.
• Experience with Kubernetes based platform for Training or Inferencing