Lead Engineer - Backend

About us:

As a Fortune 50 company with more than 400,000 team members worldwide, Target is an iconic brand and one of America's leading retailers.

Joining Target means promoting a culture of mutual care and respect and striving to make the most meaningful and positive impact. Becoming a Target team member means joining a community that values different voices and lifts each other up. Here, we believe your unique perspective is important, and you'll build relationships by being authentic and respectful.

Overview about TII:

At Target, we have a timeless purpose and a proven strategy. And that hasn’t happened by accident. Some of the best minds from different backgrounds come together at Target to redefine retail in an inclusive learning environment that values people and delivers world-class outcomes. That winning formula is especially apparent in Bengaluru, where Target in India operates as a fully integrated part of Target’s global team and has more than 4,000 team members supporting the company’s global strategy and operations.

Team Overview:

Every time a guest enters a Target store or browses Target.com nor the app, they experience the impact of Target’s investments in technology and innovation. We’re the technologists behind one of the most loved retail brands, delivering joy to millions of our guests, team members, and communities.

Join our global in-house technology team of more than 5,000 of engineers, data scientists, architects and product managers striving to make Target the most convenient, safe and joyful place to shop. We use agile practices and leverage open-source software to adapt and build best-in-class technology for our team members and guests—and we do so with a focus on diversity and inclusion, experimentation and continuous learning.

Position Overview:

We are building Machine Learning Platform to enable MLOPs capabilities to help Data scientists and ML engineers at Target to implement ML solutions at scale. It encompasses building the Featurestore, Model ops, experimentation, iteration, monitoring, explainability, and continuous improvement of the machine learning lifecycle. You will be part of a team building scalable applications by leverage latest technologies. Connect with us if you want to join us in this exiting journey.

Roles and responsibilities:

• Build and maintain Machine learning infrastructure that is scalable, reliable and efficient.
• Familiar with Google cloud infrastructure and MLOPS
• Write highly scalable APIs. Deploy and maintain machine learning models, pipelines and workflows in production environment.
• Collaborate with data scientists and software engineers to design and implement machine learning workflows.
• Implement monitoring and logging tools to ensure that machine learning models are performing optimally.
• Continuously improve the performance, scalability and reliability of machine learning systems.
• Work with teams to deploy and manage infrastructure for machine learning services.
• Create and maintain technical documentation for machine learning infrastructure and workflows.
• Stay up to date with the latest developments in technologies.

Tech stack: GCP cloud skills, GCP Machine Learning Engineer skills , GCP VertexAI skills, Python, Microservices, API development Cassandra, Elastic Search, Postgres, Kafka, Docker, CICD, optional (Java + Spring boot)

Required Skills:

• Bachelor's or Master's degree in computer science, engineering or related field.
• 9+ years of experience in software development, machine learning engineering.
• A Lead Machine Learning Engineer specializing in Google Cloud (GCP) needs a deep understanding of machine learning (ML) principles, cloud infrastructure and MLOps
• Hands-on experience with Vertex AI to manage ML platform for Feature engineering, ML training & deploying models
• VertexAI Skills needed are: BigQueyML, Automating ML workflows using Kubeflow (KFP) or Cloud composer, AI APIs, Endpoints for real-time inference, Model Monitoring, Cloud Logging & Monitoring, Cloud Dataflow for stream processing, Cloud Dataproc (Spark & Hadoop) for distributed ML workloads
• Deep experience with Python, API development, microservices. Creating ML-powered REST APIs using FastAPI, Flask, Cloud Functions
• Java (Optional, but useful for production ML systems)
• Expert in building high-performance APIs.
• Experience with DevOps practices, containerization and tools such as Kubernetes, Docker, Jenkins, Git.
• Good understanding of machine learning concepts and frameworks, deep learning, LLM etc.
• Good to have experience in deploying machine learning models in a production environment.
• Good to have experience with data streaming technologies such as Kafka, Dataflow, Kinesis, Pub/Sub etc.
• Strong analytical and problem-solving skills
• Good to have GCP certification - Professional Machine Learning Engineer