Senior AI/ML Engineer

About The Job

As a Senior AI/ML Engineer, you will take ownership of building and scaling Kuku's next-generation AI/ML systems across Personalization, Recommendation, Monetization, Ads, Generative AI, Agentic AI, and Knowledge-based AI. You will solve high-impact problems across user, content, and business intelligence, building systems that directly influence engagement, content discovery, retention, and revenue.

This role provides an opportunity to work across Machine Learning, Deep Learning, LLMs, multimodal AI, recommendation systems, predictive modeling, embeddings, knowledge systems, and agentic workflows, while owning ML systems from problem definition to production and measurable business impact.

Responsibilities

• Machine Learning & Deep Learning: Design, build, and productionise ML/DL models for prediction, classification, ranking, forecasting, embeddings, and other product/business problems.
• Personalization & Recommendation: Design and scale recommendation, retrieval, ranking, re-ranking, and personalization systems serving millions of users.
• Monetization: Build ML models and decision systems for subscription conversion, churn, LTV, pricing, coin usage, engagement, and revenue optimization.
• Ads & Marketing: Build ML systems for user targeting, campaign optimization, attribution, audience segmentation, and marketing ROI.
• GenAI & Agentic AI: Design and build LLM-powered applications and agentic workflows for content understanding, generation, analysis, automation, and decision-making.
• Knowledge-based AI: Build RAG, semantic search, knowledge bases, knowledge graphs, and embedding-based retrieval systems over Kuku's content and business data.
• Content & Multimodal AI: Develop systems to understand text, audio, video, and images, including content embeddings, metadata extraction, and multimodal understanding.
• ML Architecture & Execution: Own the architecture and execution of ML systems end-to-end, including data pipelines, experimentation, training, deployment, monitoring, and optimization.
• Technical Leadership: Drive technical direction, mentor engineers, establish best practices, and help the team solve complex ML problems.
• Research & Experimentation: Evaluate emerging ML, GenAI, and agentic techniques and translate promising approaches into production systems.
• Collaboration: Work closely with product, engineering, content, and business teams to translate ambiguous problems into scalable ML solutions with measurable impact.
  
  

Preferred Qualifications

• Education & Experience: Bachelor's or Master's in Computer Science, Machine Learning, Statistics, or a related engineering field, with 3+ years of relevant experience and a proven track record of building and deploying impactful ML/AI systems.
• ML/DL: Strong understanding of machine learning and deep learning with hands-on experience using PyTorch, TensorFlow, XGBoost, LightGBM, or equivalent.
• Personalization: Strong experience with recommendation systems, ranking, retrieval, embeddings, or user modeling is a strong plus.
• GenAI: Experience with LLMs, RAG, embeddings, prompt engineering, fine-tuning, or multimodal AI. Experience with agentic frameworks such as LangGraph/LangChain is desirable.
• Knowledge AI: Experience with semantic search, vector databases, knowledge graphs, or RAG systems is a plus.
• Monetization/Ads: Experience with churn, LTV, conversion, pricing, advertising, attribution, or growth ML is a plus.
• Production Experience: Proven experience designing, deploying, and operating production ML/AI systems at scale.
• Engineering: Strong Python and software engineering skills with experience building scalable ML systems and data pipelines. Familiarity with cloud infrastructure, Docker, Kubernetes, and MLOps is desirable.
• Technical Leadership: Experience owning complex ML projects end-to-end and mentoring engineers.
• Research Awareness: Stay up-to-date with advancements in ML, deep learning, recommender systems, GenAI, and agentic AI.
  
  

Skills: artificial intelligence,machine learning,pytorch,cloud,vector database,python,large language models,deep learning,prompt engineering,rag,ai agents,model training,natural language processing,fine tuning,embedding,peft,ml systems,generative ai,mlops,machine learning evaluation,tensorflow,xgboost,lightbgm