Senior Machine Learning Architect
4 days ago
At the forefront of machine learning innovation, we are driving cutting-edge advancements through a comprehensive approach to AI.
About This Role- This position involves spearheading applied research and development of advanced AI/ML solutions across various domains, including recommender systems, computer vision, speech processing, and generative AI agents.
- You will be responsible for architecting and deploying end-to-end ML pipelines utilizing state-of-the-art deep learning, NLP, time-series modeling, and image analysis techniques.
- The successful candidate will design scalable, maintainable AI/ML platforms that deliver robustness, reliability, and alignment with enterprise objectives.
- Additionally, you will partner with engineering, design, and product teams to operationalize ML models, ensuring seamless integration and measurable business impact.
- A strong leader is required to provide ML thought leadership, lead high-performing teams, and enforce best practices from experimentation through production deployment.
- This role also involves shaping AI strategy and guiding data-driven decision-making, influencing product innovation and operational excellence across the organization.
- A strong leader is required to provide ML thought leadership, lead high-performing teams, and enforce best practices from experimentation through production deployment.
- Additionally, you will partner with engineering, design, and product teams to operationalize ML models, ensuring seamless integration and measurable business impact.
- The successful candidate will design scalable, maintainable AI/ML platforms that deliver robustness, reliability, and alignment with enterprise objectives.
- You will be responsible for architecting and deploying end-to-end ML pipelines utilizing state-of-the-art deep learning, NLP, time-series modeling, and image analysis techniques.
Our ideal candidate has:
- A graduate/post-graduate degree from a reputable institution with 5–8 years of applied experience in AI/ML, spanning NLP, deep learning, computer vision, image processing, and with 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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