Role overview
- Lead design, development, and deployment of Generative AI, LLM, and ML applications in collaboration with business teams as a forward-deployed AI engineer.
- Partner with product and business stakeholders to shape AI strategy, define use cases, and translate requirements into scalable, high-impact AI solutions.
- Champion system design and software engineering best practices, ensuring solutions are robust, extensible, and secure.
- Utilize frameworks such as LangChain, HuggingFace, Vertex AI, and other enterprise-scale AI platforms to build forward-looking capabilities.
- Drive efficiency in AI development by leveraging AI-assisted development tools, automation, and reusable frameworks.
- Own the end-to-end AI delivery lifecycle: experimentation, fine-tuning, deployment, monitoring, and continuous improvement.
- Provide technical leadership, mentorship, and guidance to AI Engineers, advocating responsible AI principles and strong engineering culture.
- Ensure seamless integration of LLMOps and MLOps practices for production-ready deployments, including prompt engineering, fine-tuning, monitoring, and optimization.
- Stay ahead of AI trends, evaluating new models, architectures, and tools to maintain competitive advantage.
What we're looking for
- Advanced proficiency in Python, particularly in object-oriented programming, API development (e.g., using Flask), and working with SQL - Required
- Deep expertise in large language models (LLMs) , customer LLM framework , Agentic AI and familiarity with best practices in LLMops, alongside substantial experience with MLOps frameworks such as Vertex AI - Required
- Extensive experience with NLP frameworks and libraries such as SpaCy, Transformers, HuggingFace, LangChain, as well as familiarity with other tools (PyTorch, TensorFlow, scikit-learn, NLTK) and techniques including topic modeling, named entity recognition, and information retrieval - Required
- Practical experience with large language model prompting techniques, including zero-shot, few-shot, and chain-of-thought strategies - Required
- Experience with modern Python development practices including type checking, testing frameworks, and package management - Required
- Proven experience working within cloud computing environments, ideally on Google Cloud Platform (GCP), and a basic understanding of Infrastructure as Code (IaC) for cloud setups - Required
- Familiarity with ML Development Lifecycle management and MLOps best practices - Required
- Exceptional leadership, communication, and interpersonal skills, with a strong commitment to mentoring colleagues and fostering a diverse, equitable, and inclusive work environment - Required
- Understanding of ML/AI platform tooling and patterns - Preferred
- Not offered, now or in the future
Tags & focus areas
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