Chief AI Architect
1 week ago
We are seeking an innovative and forward-thinking Lead Data Scientist to spearhead cutting-edge AI/ML initiatives. This unique opportunity allows for comprehensive exposure to various ML technologies, enabling you to broaden your skill set and drive significant impact.
- Spearhead applied research and development of advanced AI/ML solutions across diverse domains including recommender systems, computer vision, speech processing, and generative AI agents
- Architect and deploy end-to-end ML pipelines leveraging state-of-the-art deep learning, NLP, time-series modeling, and image analysis techniques
- Design scalable, maintainable AI/ML platforms that deliver robustness, reliability, and alignment with enterprise objectives
- Partner with engineering, design, and product teams to operationalize ML models, ensuring seamless integration and measurable business impact
- Provide ML thought leadership and lead high-performing teams, enforcing best practices from experimentation through production deployment
- Shape AI strategy and guide data-driven decision-making, influencing product innovation and operational excellence across the organization
- Provide ML thought leadership and lead high-performing teams, enforcing best practices from experimentation through production deployment
- Partner with engineering, design, and product teams to operationalize ML models, ensuring seamless integration and measurable business impact
- Design scalable, maintainable AI/ML platforms that deliver robustness, reliability, and alignment with enterprise objectives
- Architect and deploy end-to-end ML pipelines leveraging state-of-the-art deep learning, NLP, time-series modeling, and image analysis techniques
Requirements:
- Graduate/Post-graduate from a top-tier institute with 5–8 years of applied experience in AI/ML, spanning NLP, deep learning, computer vision, image processing, and introductory experience in building or fine-tuning generative AI models.
- Proven leadership (2+ years) in leading teams and driving ML projects, with a track record of building strong Data Science practices and fostering a high-performance culture.
- Advanced ML Expertise: Proficiency in ML frameworks (TensorFlow, PyTorch, Keras, scikit-learn) and Python, with hands-on experience in deep learning architectures (Transformers, CNNs, RNNs, LSTMs), statistical modeling, and scalable deployment using distributed systems (e.g., Spark, Hadoop).
- Generative AI & Applied Innovation: Practical experience with LLM fine-tuning, RAG pipelines, embeddings, and prompt engineering; familiarity with GenAI ecosystems (LangChain, Langraph, Pinecone, CrewAI) and integration into production applications.
- End-to-End Impact & Collaboration: Strong background in data wrangling, feature engineering, and MLOps, combined with proven ability to collaborate across engineering, product, and business teams and communicate complex concepts effectively.
- A passion for solving complex problems with the courage to take research risks and drive innovation.
- End-to-End Impact & Collaboration: Strong background in data wrangling, feature engineering, and MLOps, combined with proven ability to collaborate across engineering, product, and business teams and communicate complex concepts effectively.
- Generative AI & Applied Innovation: Practical experience with LLM fine-tuning, RAG pipelines, embeddings, and prompt engineering; familiarity with GenAI ecosystems (LangChain, Langraph, Pinecone, CrewAI) and integration into production applications.
- Advanced ML Expertise: Proficiency in ML frameworks (TensorFlow, PyTorch, Keras, scikit-learn) and Python, with hands-on experience in deep learning architectures (Transformers, CNNs, RNNs, LSTMs), statistical modeling, and scalable deployment using distributed systems (e.g., Spark, Hadoop).
- Proven leadership (2+ years) in leading teams and driving ML projects, with a track record of building strong Data Science practices and fostering a high-performance culture.
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