We’re looking for**Senior Technical Consultants (AI & Data)** with deep expertise in the following domains: Data Engineering, AI Solution Architecture, and Machine Learning & Data Science. This is a senior consulting role combining technical depth, client-facing capability, and the ability to shape and deliver data-driven solutions. Mentoring internal teams, contributing to knowledge-sharing initiatives, and strengthening intive’s consulting practice 5–7+ years of hands-on experience in AI/Data projects, with proven success delivering solutions for enterprise clients Ability to lead technical assessments, define solution architectures, and guide clients through implementation decisions Strong communication skills, including leading workshops, translating complex technical topics into business insights, and building long-term client relationshipsAbility to mentor teams, set delivery direction, and ensure alignment between business goals and technical strategies Strong problem-solving skills with the ability to shape consulting offerings and drive new opportunities Advanced programming skills (Python) Proficiency in at least one major cloud platform Proven ability to design and optimize large-scale data pipelines, data architectures, and analytical platforms Experience with modern data ecosystems: cloud-native data pipelines, data warehouses/lakes, streaming systems Advanced programming skills and strong understanding of distributed systems Hands-on experience with DataOps/MLOps-adjacent tooling, orchestration frameworks, CI/CD, and production-grade data infrastructure Experience applying generative AI, LLMs, vector databases, embedding-based retrieval, and modern AI system patterns Ability to define technical strategies, lead deep-dive workshops, and evaluate emerging technologies for client use cases *Machine Learning & Data Science** Familiarity with generative AI, recommendation systems, RAG architectures, and applied LLM techniquesAdvanced experience building and deploying ML models, including classical ML and deep learning approaches Strong analytical and statistical skills, as well as comfort turning raw data into actionable insights Experience with ML lifecycle: feature engineering, training pipelines, evaluation frameworks, and production deployment
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