Digital and Technology India, is seeking an Machine Learning Engineer II to join the Enterprise Data Capabilities Organization. This team builds enterprise-level scalable and sustainable data and model pipelines to serve the analytic needs of business and high-impact problem statements. In this role, you are a critical member of the data science team focused on operationalizing the ML and AI models, which entail model management and monitoring too. The success is to recommend innovative ways to automate the MLOps pipelines on GCP and set standards that would ensure repeated success.

This capability is leveraged to fuel advanced AI solutions, Machine Learning and Deep Learning. It is also responsible for implementing and enhancing the community of practice to determine the best practices, standards, and MLOps frameworks to efficiently delivery enterprise data solutions.

This role works in close collaboration with Data Scientists, Data Engineers, Platform Engineers and Tech Expertise to support the analytic consumption needs. Enhances the performance of the models and automates the production pipelines to gain efficiency.

Role Responsibilities :

- Implement MLOps practices :

i. Implementation of end-to-end MLOps framework and Machine Learning Pipeline using GCP, Vertex AI and Software tools on the assigned project(s).

ii. Assume complete ownership of assigned ML Ops tasks and perform them with high quality adhering to project timelines with minimal external supervision

iii. Development of feature engineering pipelines including config, ingestion and transformation of data from multiple sources using tools like BigQuery, Dbt & Google cloud storage (GCS), etc.

iv. Setup Meta Data and Data statistics curation using GCP Bucket and ML Metadata (MLMD)

v. Re-Training and Monitoring Pipeline setup with multiple criteria Vertex AI

vi. Development of Serving Pipeline with Vertex AI and GCP services

vii. Resource and Infra Monitoring configuration and pipeline development using GCP

viii. Automated pipeline Development for Continuous Integration (CI)/Continuous Deployment (CD) Continuous Monitoring (CM)/Continuous Training (CT) using GCP-native tool

ix. ML Pipeline orchestration and configuration using airflow/cloud composer/Kubeflow.

x. Code refactorization & coding best practices implementation as per industry standard

xi. Support the ML models throughout the E2E MLOps lifecycle from development to maintenance.

xii. Comprehensive documentation to support all stages of ML Ops

- Recommend any changes required to the existing MLOps practices

- Communication and Collaboration :

i. Collaborate with technical teams like Data Science, Data Engineering, and Cloud Platforms, etc.

ii. Partner with MLOps Domain leads to ensure adoption and implementation of MLOps best practices.

iii. Knowledge sharing with the broader analytics team and stakeholders is

iv. Active up-to-date Communication on the in-flight projects to embrace the remote and cross geography

v. Align the key priorities and focus

vi. Ability to communicate accomplishments, failures, and risks in a timely manner.

- Embrace a learning mindset :

i. Continually invest in upskilling through formal training, reading, hands-on training, and attending conferences and meetups

- Documentation :

i. Document MLOps Process, Development, Architecture & Innovation etc and be instrumental in reviewing the same for other team members.

Must - have technical skills and experience :

- Minimum qualification- Bachelors degree (full time)

- Total professional analytics experience required of 6+ Years

- Expertise and at least 3+ years of professional experience in AI and Machine Learning

- Expertise in Data Transformation and Manipulation through Big-Query/SQL

- Professional experience with Vertex AI and GCP Services

- Strong expertise in Python for designing and running ML pipelines

- Airflow/Cloud composer/Kubeflow Experience

- Building and maintaining project specific custom containers

- Strong communication skills both verbal and written including the ability to interact effectively with colleagues of varying technical and non-technical

Good to have skills :

- Google Cloud Platform Machine Learning (GCPML) certification

- Understanding of the Consumer-Packaged Goods (CPG) industry

- Strong understanding of Core Machine Learning Algorithms

Skill proficiency expectations :

Expert level :

- ML Ops framework

- Big Query/SQL

- Python

- Vertex AI and GCP Services

- Docker-Container

- Kubeflow/Kubernetes