Machine Learning Engineer | Recommendation Systems, LLMs, Agentic AI | Google-Backed | 400M+ Downloads
- 90K-120K
- Bengaluru, On-site
Full Job Description
About the Company
AI Commerce Pioneer | Redefining Shopping with Generative AI & Intelligent Personalization
Venture-backed by Google and Jio Platforms, this groundbreaking consumer technology company is transforming digital commerce through AI-first innovation. The company powers intelligent lock screen experiences and live entertainment for millions globally. Building an industry-defining AI commerce platform that combines predictive intelligence, neural visualization, and real-time orchestration across mobile, TV, and app ecosystems.
Vision: Reimagining shopping and content discovery through generative AI, autonomous systems, and next-generation personalization at unprecedented scale.
Job Description
Seeking a Machine Learning Engineer to design, build, and deploy large-scale recommendation systems and agentic AI solutions. Blend classical ML, deep learning, and autonomous AI to deliver personalized, intelligent experiences across mobile, TV, and apps.
Responsibilities:
Classical ML & Recommendation Systems
Design and implement large-scale recommendation systems using ML, ranking algorithms, embeddings, and deep learning.
Build and operate ML models for personalization, prediction, and content understanding.
Develop rapid experimentation pipelines to validate hypotheses and measure impact.
Manage end-to-end data preparation, model training, evaluation, and deployment pipelines.
Monitor model performance, detect drifts, and optimize algorithms for better user experience.
Agentic Systems & Next-Gen AI
Build agentic AI systems that autonomously optimize workflows, hyperparameters, and ranking policies.
Apply LLMs, RAG architectures, embeddings, and multimodal generative models for semantic understanding and content classification.
Design intelligent agents to automate decision-making tasks, feature selection, and context-aware personalization.
Explore reinforcement learning, contextual bandits, and self-improving ML systems.
Cross-functional Impact
Collaborate with Designers, UX Researchers, Product Managers, and Engineers to integrate ML/AI features into consumer experiences.
Contribute to thought leadership via blogs, case studies, and industry forums.
Align ML initiatives with strategic priorities across product, business, and infrastructure teams.
Required Skills & Experience:
6+ years in ML/Data Science with hands-on experience in large-scale recommendation systems or personalization.
Expertise in Python, PyTorch/TensorFlow, and statistical modeling (R, NumPy, SciPy).
Strong knowledge of LLMs, generative AI, agentic AI systems, and RAG architectures.
Experience with deep learning, NLP, reinforcement learning, time series, and clustering.
Proficient in cloud platforms (AWS, GCP/Vertex AI, Azure) and big data ecosystems (Spark, Hadoop).
Proven track record deploying ML pipelines in production environments.
Excellent problem-solving, communication, and cross-functional collaboration skills.
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