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Salary
≈ $93k – $165k per year (Estimated)
Location
In office (London)
Seniority
Senior
Employment
Full-Time

Confirmed on the employer's own hiring board on Oct 31, 2026. First seen by Alion on Jun 7, 2026. DataArt scores B on the Alion truth index.

Overview
Company
Impact
Profile match
DataArt is a global software engineering and consulting firm headquartered in New York that builds and modernises custom software, data platforms and cloud infrastructure for clients in finance, travel, healthcare, media and retail. Founded in 1997, it runs delivery centres across Europe, the Americas and Asia, including offices in Wroclaw, Lublin and Lodz in Poland and teams in India, and works with more than 400 enterprise clients. Its openings include senior .NET, Java, Python and frontend engineers, DevOps and site reliability engineers, data architects and data engineers, QA automation leads and solution consultants.

DataArt’s AI Lab helps organizations turn AI from experimentation into transformation - with practical, scalable solutions that drive real business outcomes. We combine strategic advisory, deep engineering, and reusable delivery accelerators through our Connect AI framework to enable smarter operations, faster decision-making, and sustainable growth.

As part of our AI & ML team, you’ll lead and contribute to AI strategy and implementation initiatives - from roadmap design and maturity assessments to co-building pilots and reusable solution patterns. You’ll work across industries and functions, applying GenAI, data science, and modern cloud-native architectures to solve complex, high-impact challenges. This role blends strategic thinking with hands-on execution, and offers the opportunity to shape how AI is delivered at scale.

DataArt AI Lab is looking for a Senior AI Consultant to join its core team and support a range of initiatives focused on AI-driven transformation. The role involves close collaboration with AI Lab leadership and cross-functional teams to design and implement AI solutions that enhance productivity, enable smarter workflows, and drive innovation across different domains and industries.

  • Lead AI strategy engagements from opportunity discovery through roadmap development, aligning AI adoption with client business goals, tech stack, and operating model
  • Shape reusable solution architectures using modular components, copilots, and delivery accelerators from the Connect AI framework to ensure scalable, cross-client value
  • Translate complex AI capabilities into business impact, working closely with CxO-level stakeholders to prioritize initiatives and quantify ROI
  • Design and facilitate client workshops to align on goals, surface high-impact use cases, and assess AI maturity across technical, organizational, and data dimensions
  • Contribute to DataArt’s evolving AI offerings, including vertical accelerators, governance models, and productized services with pricing and go-to-market relevance
  • 3-5+ years in AI, digital transformation, or strategy consulting, ideally in client-facing roles within tech-driven or innovation-led engagements
  • Proven ability to define enterprise AI strategies and roadmaps, including maturity assessments, capability planning, and use case prioritization
  • Strong business and technical fluency, with the ability to bridge AI/ML concepts and modern architectures with measurable executive-level business value
  • Experience designing reusable toolkits, delivery frameworks, or productized services - ideally contributing to IP-backed consulting models
  • Familiarity with GenAI ecosystems (e.g., LLMs, RAG, prompt engineering, agents) and their application across industry verticals and business functions
  • Solid understanding of technical principles and architecture of ML/LLM applications and AI agents
  • Hands-on experience with major cloud platforms (e.g., AWS, GCP, Azure) and their AI/ML service offerings
  • Strong communication skills and effective stakeholder management
  • Master’s degree in Computer Science, AI, Machine Learning, or a related field
  • Exposure to AI monetization models, including usage-based pricing, enablement fees, or outcome-driven commercial constructs
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