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Tier-1 brand plus mid-level generalist experience but niche ML/LLM tooling requirements increase competition.
Specialized ML infra and retrieval skills transferable across industries but still require domain-specific experience.
Explicit 2-4 years plus mandatory ML, LLM, vector DB, cloud, and MLOps skills drives high strictness.
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Develop and maintain ML model evaluation frameworks and inferencing pipelines for AI/ML models powering search and context graph systems at scale.
Implement AI automation workflows and collaborate with engineering teams to integrate AI/ML into distributed microservices, enhancing semantic search and vector embedding features.
Participate in production incident management, monitoring, troubleshooting, and SLA adherence for AI-powered services.
Bachelor's or Master's in Computer Science, Machine Learning, or related field.
2-4 years of experience building and operating ML systems or AI-powered applications.
Proficiency in Python, ML fundamentals (training, evaluation, inference, deployment), experience with LLM APIs, prompt engineering, vector databases, and cloud platforms like AWS.
Familiarity with ML frameworks such as PyTorch, TensorFlow, or Hugging Face Transformers.
Experienced in AI/ML engineering with a focus on scalable production systems in search and context graph domains.
Comfortable working with foundation models, embedding-based retrieval, cloud infrastructure, and MLOps pipelines.
Effective communicator capable of articulating complex AI/ML concepts and collaborating across engineering teams.