Accountabilities:

Design, develop, deploy, and maintain machine learning components within real-time search serving systems, ensuring performance under strict latency requirements.
Build query-time machine learning capabilities that improve search understanding, query interpretation, and the overall relevance of search results.
Develop and scale machine learning and LLM data pipelines across large product catalogs, ensuring data freshness, processing efficiency, and cost optimization.
Optimize machine learning models across different merchants, business verticals, languages, and geographical markets.
Build and scale online features, model-serving infrastructure, and production systems that support high-volume search and discovery applications.
Establish offline evaluation frameworks and safe production rollout processes, including baseline comparisons, A/B testing, fallback mechanisms, and latency and cost monitoring.
Improve observability, monitoring, and diagnostic capabilities to identify performance issues and maintain the reliability of production machine learning systems.
Collaborate with Search Quality and engineering teams to integrate advanced relevance and ranking models into production environments while meeting performance and cost targets.
Analyze model and system performance, identify technical bottlenecks, and implement improvements across both machine learning and backend engineering components.
Contribute to technical architecture discussions, influence engineering decisions, and promote scalable software development and machine learning best practices.
Take end-to-end ownership of technical challenges, from problem analysis and solution design through implementation, deployment, and ongoing optimization.

Requirements

Bachelor's or Master's degree in Computer Science or a related technical discipline, such as BE/B.Tech or ME/M.Tech, with a strong foundation in mathematics and programming.
At least 5 years of professional experience building, deploying, and maintaining machine learning-driven systems in production environments.
Excellent Python programming skills and practical experience with backend programming languages such as Go or Java.
Strong software engineering expertise, including hands-on experience deploying and serving machine learning models and LLMs within scalable cloud environments.
Experience working with cloud platforms such as AWS or Google Cloud Platform (GCP), particularly when managing systems with strict latency, scalability, and infrastructure cost requirements.
Proven experience designing, developing, and maintaining large-scale data processing and machine learning pipelines.
Strong analytical and problem-solving abilities, with the capacity to break complex challenges into machine learning and backend engineering components, investigate root causes, and identify effective solutions.
Experience working across multiple customers, business domains, languages, or large-scale datasets.
Working knowledge of search technologies and concepts, including query understanding, relevance ranking, autocomplete, autosuggest, and faceted search.
Hands-on experience with deep learning frameworks such as PyTorch or TensorFlow.
Strong understanding of production machine learning systems, model evaluation, deployment strategies, and performance optimization.
Excellent collaboration and communication skills, with the ability to work effectively across multidisciplinary engineering teams and contribute to technical discussions.

Preferred Qualifications

Experience with search engines such as Solr, Elasticsearch, or similar technologies.
Practical experience deploying vector search solutions in production using technologies such as Milvus or Qdrant.
Experience building evaluation frameworks, observability tools, and explainability solutions for machine learning models, LLMs, and model-serving infrastructure.
Familiarity with advanced AI-powered search, recommendation, and personalization technologies.
A proactive mindset, strong technical ownership, and enthusiasm for exploring emerging machine learning technologies.

How Jobgether works

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

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