ML Engineer

3 weeks ago

Bengaluru, India Lilly Full time
We’re looking for people who are determined to make life better for people around the world.
At Lilly, we unite caring with discovery to make life better for people around the world. We are a global healthcare leader headquartered in Indianapolis, Indiana. Our 39,000 employees around the world work to discover and bring life-changing medicines to those who need them, improve the understanding and management of disease. We’re looking for people who are determined to make life better for people around the world.
The LCCI (Lilly Capability Center India) Business Insights & Analytics team was started in 2017 with the objective of using innovative data mining and analytics to support business decisions to Marketing functions in the US and ex-US affiliates (focused on in-line and pre-launch brands). This team has rapidly grown and currently comprises of more than 100 staff members, with varied backgrounds and skills across data management, data sciences, analytical techniques, pharmaceutical commercial operations, and business insights. The team provides analytics outcomes for driving decision making across Lilly’s Marketing, Sales, Medical Affairs, and a range of other functions.
To support the marketing teams in their decision-making, a data and analytics team has been set up simultaneously in Indianapolis (HQ) and Bengaluru (LCCI). This team is responsible for setting up the data warehouses necessary to handle large volumes of digital streaming data, create meaningful analyses using that data, and deliver recommendations to leadership.
As part of the LCCI team, we have an exciting opportunity for the role of an ML Engineer who will be an integral part of the ML Ops pillar within BI&A. The purpose of this role is to manage projects requiring ML Ops expertise across the commercial analytics continuum (incl Dynamic Targeting, HCP analytics etc.) helping drive customer experience and business impact.
Core Responsibilities:
· Responsible for the ML Ops pipelines creation and automation on cloud and premise, CI/CD pipelines orchestration across multiple projects
· ML models code refactoring, training, retraining, deployment, testing and continuous monitoring for drift.
· Applying software engineering rigor and best practices to machine learning, including CI/CD, automation, etc
· Optimisation of model hyper parameters
· Evaluation and explicability of models
· Model onboarding, operations, and decommissioning workflows.
· Creating and maintaining scalable MLOps frameworks to support client-specific models.
· As MLOps expert, providing technical design solutions to support PoVs.
· To measure and improve services, create and use benchmarks, metrics, and monitoring.
· Providing best practises and running proof-of-concepts for automated and efficient model operations on a large scale.
· Coordinate with diverse stakeholders such as statisticians, software engineers, infrastructure teams to better understand requirements and constraints to design the most optimal ML pipelines
Required:
· 4-8 years of demonstrated expertise in building ML/automation pipelines from scratch (Including model versioning, model and data lineage, monitoring, model hosting and deployment, model optimization, scalability, orchestration, continuous learning, Automated pipelines)
· Should be proficient in AWS components such as Sage maker, AWS Lambda, other AWS services, serverless services, etc.
· Strong knowledge of python and PySpark. Working knowledge of R is preferred
· Strong knowledge of working tools like Docker, Kubernetes, Jenkins
· Experience in using popular MLOps frameworks like Kubeflow, MLFlow, and DataRobot
· Knowledge of frameworks such as scikit-learn, Keras, PyTorch, Tensorflow, etc. and ability to understand tools used by data scientist
· Some Hands -on the table’s creations, DDL, DML and TCL
· Experience with AWS cloud services: EC2, EMR, RDS, Redshift, S3, Athena is ad advantage
· Experience with data pipeline and workflow management tools: Airflow etc.
· Experience developing new components in a scrum/agile environment
· Excellent verbal and written communication skills with the ability to effectively advocate technical solutions to data scientists, engineering teams and business audiences.
Education:
· Bachelor’s or Master’s in Computer Applications/Computer Science OR Specialization/courses in ML/DS, having deep understanding of SDLC.
Lilly does not discriminate on the basis of age, race, color, religion, gender, sexual orientation, gender identity, gender expression, national origin, protected veteran status, disability or any other legally protected status.
#WeAreLilly

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