Head of Engineering (Data, ML and AI Tools)
As Head of Engineering for Data, ML & AI Tools, you will lead multiple high-impact teams building AI-powered tools, voice interfaces, analytics products, and machine learning models that support utility clients and internal teams. You’ll manage Engineering Leads and collaborate with cross-functional teams to define technical strategy, deliver production-grade systems, and drive innovation across LLM, data, and ML domains. Your teams will deliver agent-assist tools like AnswerBots, customer-facing VoiceBots, predictive models for utility use cases (e.g., water leakage detection, churn), and client dashboards turning data into actionable insights.
What you'll do
* Lead and scale cross-functional ML and data engineering teams, including machine learning engineers, LLM/AI engineers, data analysts, and software engineers
* Drive the development of intelligent tools for customer agents (e.g., LLM-based AnswerBots) and end-users (e.g., VoiceBots) that improve service efficiency and experience
* Guide the team delivering predictive ML models for utility clients in areas like water leak detection and churn prediction, ensuring robustness, explainability, and client value
* Oversee the development of data analytics products – including dashboards and data pipelines – that deliver actionable intelligence to utility clients
* Set the long-term technical vision for our AI & analytics products while being hands-on in system design, architecture reviews, and high-impact technical decisions
* Partner closely with cross-functional stakeholders, including product managers, customer teams, and delivery leads, to align technical solutions with business goals
* Champion experimentation, prototyping, and fast iteration to explore new use cases with GenAI and classic ML across business domains
* Build systems that combine structured data, unstructured customer interactions, and real-time operational signals, powering the next generation of intelligent decision support
* Stay on the forefront of emerging technologies in LLMs, RAG (Retrieval-Augmented Generation), reinforcement learning, and agentic workflows — and translate them into practical, scalable products
What you'll need
* Proven leadership experience in managing multi-disciplinary engineering teams (20+ engineers), including ML, LLM, data engineering, and platform engineering
* Understanding of ML lifecycle – from data ingestion and feature engineering to model training, evaluation, and deployment in production
* Hands-on experience deploying LLM-based systems (e.g., RAG pipelines, tool calling, fine-tuning, RLHF) and integrating them into real-world applications
* Experience developing customer-facing AI tools, voice-based interfaces, or agent augmentation systems is highly desirable
* Strong architectural skills and the ability to make pragmatic decisions between prototypes and production-grade systems
* Experience building and operating robust data pipelines, ETL frameworks, and analytics dashboards that serve clients in regulated industries like water or telco
* Familiarity with modern AI/ML stacks: Python, Kubernetes, PyTorch, LangChain, vector databases, etc
* Demonstrated ability to align AI and analytics initiatives with business outcomes and customer value
* Inspirational leadership style with a strong product sense and ability to lead through ambiguity. Experience managing teams of teams (20+ engineers is desired)
Kraken is an equal opportunity employer. We welcome applicants from all backgrounds. For accommodations in the interview process, contact inclusion@kraken.tech.
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