About The Positions  Contributions:

Accountabilities, Actions and Expected Measurable Results (70%)

You understand how to design and build scalable distributed systems, backend platforms, compatible with AI/ML infrastructure for search, retrieval, ranking, recommendation, and personalization use cases

 You will:

• Design and build systems, manage scalable ML pipelines using Vertex AI Pipelines for training, evaluation and deployment to support ranking, retrieval, and recommendation personalization use cases

• Develop and maintain data pipelines that support feature generation, model training, and analytics workflows. Own vector generation via Milvus, storage, and retrieval workflows

• Implement model serving solutions using KServe and build APIs using FastAPI for low latency inference

• Build observability and monitoring for models and pipelines. Track performance, drift, failures, and data quality issues

• Collaborate with data scientists, product managers, and platform teams to define and deliver ML driven features

• Investigate production issues across data pipelines, models, and services. Identify bottlenecks and improve reliability and performance

• Create and maintain clear documentation for pipelines, models, APIs, and operational processes

• Develop internal tools and dashboards to provide visibility into data processing and model behavior for stakeholders

• Contribute to engineering standards, code quality, and best practices across Python-based services and ML systems

• Stay current with ML infrastructure, MLOps practices, and relevant tools. Bring in improvements where they add clear value

Collaborate with product, data science, and frontend teams to deliver high quality search and feed experiences (30%)

• Own production systems. Debug issues across indexing, retrieval, ranking, and serving layers

• Create clear documentation for pipelines, models, APIs, and system design

• Contribute to best practices for Python based ML systems, API design, and scalable infrastructure

• Stay current with advancements in search, ranking, and recommendation systems. Apply them where they make practical impact

The Role’s Minimum Qualifications and Job Requirements

Education:

Bachelor’s degree in Computer Science, Engineering, or a related field

Experience:

You have a strong foundation in modern backend and ML engineering practices and continue to learn and evolve. You bring:

• 6+ years of experience building scalable backend systems and services

• 5+ years of experience developing software using object oriented languages, with strong proficiency in Python, Node.js, and TypeScript

• Hands on experience with ES for search, indexing, and relevance tuning

• Experience with event driven systems using Apache Kafka for real time data pipelines and processing

• Strong understanding of version control systems including Git and platforms like Bitbucket

• Experience with observability and monitoring tools such as Grafana, Kibana, and APM

• Familiarity with cloud platforms including AWS and GCP, along with containerization using Docker and orchestration with Kubernetes

• Comfortable deploying, versioning, and monitoring models in production

• Curiosity to learn new technologies, especially in AI, LLMs, and modern search and recommendation systems, with a focus on applying them in real production use cases.

• Experience designing and building data pipelines using Apache Beam and Apache Airflow for ingestion, transformation, and feature pipelines

• Familiarity with experimentation and analytics tools such as Jupyter Notebook and Apache Spark to track and reproduce experiments

• Strong experience designing and consuming RESTful and GraphQL APIs, including versioning, documentation, and security practices like OAuth and JWT

• Good understanding of machine learning concepts including supervised learning, unsupervised learning, deep learning, and natural language processing, with practical application in ranking, retrieval, and personalization

• Beginner level experience managing ML pipelines using Vertex AI Pipelines for training, evaluation, and deployment workflows

• Ability to review code, provide clear feedback, and improve overall engineering quality

• Strong communication skills. Able to explain technical concepts clearly to both technical and non technical stakeholders

• Solid problem solving skills with a data driven approach

Specific Knowledge, Skills, Certifications and Abilities:

Core Tech Stack

• Backend and API development using Python, FastAPI, Node.js, and TypeScript

• Search and indexing using Elasticsearch for relevance, retrieval, and query optimization

• Event driven architecture and streaming using Apache Kafka

• Vector search and embeddings infrastructure using vector databases such as Milvus or Pinecone

• Cloud and infrastructure using Google Cloud Platform or Amazon Web Services with containerization via Docker and orchestration through Kubernetes

% Travel Required (Approximate): 0 %