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Tier-1 brand plus broad GenAI skillset increases applicant density, but seniority reduces competition to medium.
Deep generative AI and ML engineering requirements make cross-industry transfers difficult, so background sensitivity is high.
Multiple explicit years requirements, Master's degree, and mandatory GenAI, ML frameworks, and cloud skills create high shortlisting strictness.
Develop and implement AI, Machine Learning, and Generative AI strategies focused on marketing-related business domains and content supply chains.
Design, build, and deploy scalable AI/ML and Generative AI solutions using technologies like LLMs, RAG, AI Agents, Vector Databases, and prompt engineering, ensuring integration across cloud platforms.
Lead AI/ML initiatives following Agile and DevSecOps practices, mentor teams, and drive adoption of AI engineering standards and governance.
Master's degree in Computer Science, Engineering, Mathematics, Statistics, or related quantitative discipline.
12+ years of experience in Software Engineering, Data Science, Analytics, or related fields.
8+ years of hands-on experience in AI/ML Engineering.
3+ years of recent experience with Generative AI technologies such as OpenAI, Claude, Gemini, LangChain, AI Agents, RAG, Vector Databases, Prompt Engineering, and model fine-tuning.
Experienced in delivering AI-driven products with a strong background in marketing technology or digital marketing analytics.
Proficient in modern AI/ML frameworks (MLflow, TensorFlow, PyTorch, Keras, Scikit-learn) and cloud platforms (AWS, Azure, GCP).
Skilled in implementing Agile delivery, DevSecOps, CI/CD pipelines, and automation in AI development lifecycle.