AI & Analytics - Data & AI Solutions Lead

Key Responsibilities

• Opportunity Identification & Solution Proposals Identify new opportunities to apply data analytics and AI services across business functions and client engagements and proactively surface them to leadership
• Collaborate with stakeholders to evaluate the business value, feasibility, and priority of each opportunity, translating ambiguous needs into clearly defined problem statements
• Develop comprehensive proposals and RFP/RFI responses covering scope, approach, architecture, staffing, man-day estimates, timeline, and expected impact, and present them persuasively to technical and non-technical audiences
• Build the business case for investment, including the trade-offs and risks of each proposed approach
• Lead scoping and discovery workshops with clients, and manage scope changes and contract amendments when requirements evolve beyond the agreed perimeter
• Solution Design & Delivery Design custom data and AI solutions tailored to the defined business need including multi-agent pipelines, RAG systems, and LLM-powered workflows selecting appropriate tools, frameworks, architectures, and models
• Own the deployment of solutions into client and cloud environments working with the team on CI/CD pipelines, containerization, environment setup, and post-deployment monitoring so that solutions run reliably in production Define how solution quality is measured (e.g. accuracy, coverage, reliability), build evaluation and validation loops for LLM and agent outputs, monitor performance in production, and drive continuous enhancement based on observed results and user feedback
• Ensure solutions are built to be maintainable, scalable, and reliable rather than optimized for a single successful demonstration
• Manage the cost and performance of AI workloads token and compute consumption, cloud spend, latency and make architecture trade-offs that keep solutions economically sustainable
• Build solutions that respect data privacy, security, and responsible AI requirements by design, particularly for public-sector and regulated clients
• Technology Scouting & Standards Stay informed on the latest business applications, tools, and methods in data analytics and AI, including LLMs, agentic frameworks, and deployment, MLOps, and LLMOps platforms
• Evaluate emerging technologies against Infomineo's actual needs and introduce those that offer genuine advantage, with a clear rationale for adoption
• Define and champion technical standards and best practices for how data and AI solutions are built, evaluated, and deployed, in collaboration with department and Tech/R&D leadership
• Project Leadership Lead projects end-to-end, coordinating priorities across concurrent initiatives and allocating resources to match business impact
• Communicate progress, risks, dependencies, and trade-off decisions clearly and proactively to the Head of AI & Analytics Services, clients, and wider stakeholders
• Anticipate delivery risks early and drive resolution rather than escalating them unresolved
• Plan team capacity and staffing across concurrent engagements, matching people to projects based on skills, workload, and business impact, and flag resourcing gaps early
• Define and maintain the team's delivery rituals, estimation practices, quality and review routines, and escalation paths, and improve them as the team grows
• Team Development & Mentorship Serve as a mentor and coach to the team, providing guidance, training, and career development opportunities
• Manage the performance and development of team members regular 1:1s, feedback, performance conversations, objective setting, and career growth planning
• Contribute to hiring, onboarding, and retention for the team: defining profiles, interviewing candidates, and integrating new joiners effectively
• Recognize strong performance and address underperformance constructively and early
• Teach team members how to approach problems effectively how to frame a question, choose an approach, and validate a result and empower them to apply these strategies independently
• Build a culture of technical rigour, intellectual curiosity, and shared ownership of outcomes
• Professional Standards, Security & Responsible AI Uphold client confidentiality, data protection, information security, and company policies across all engagements, and ensure the team does the same
• Apply responsible AI practices transparency on model limitations, human oversight, and appropriate use of client data and raise concerns early when a use case warrants it
• Desired Candidate Profile

Qualifications

• Proven track record in data analytics, data science, or AI solution delivery, with experience owning delivery end-to-end and guiding the work of others typically built over 6 or more years, though readiness is assessed on demonstrated capability rather than years alone
• Demonstrated experience delivering technical solutions in a client-facing or stakeholder-facing context, with exposure to a range of technical solution types rather than a single repeated use case
• Solid working knowledge of DevOps practices (CI/CD, Docker, cloud environment configuration, monitoring), with the ability to own deployment end-to-end alongside the team
• Proven ability to develop and present solution proposals that translate a business need into a defined technical approach, scope, and value case
• Hands-on experience designing and delivering LLM-based and agentic AI solutions in production (e.g. multi-agent pipelines, RAG, tool use), using orchestration frameworks such as LangGraph, LangChain, or equivalent, alongside strong working knowledge of machine learning
• Experience defining evaluation methods for LLM and agent outputs (test sets, quality metrics, human or model-based review) and using them to drive improvement
• Ability to reason about and optimize the cost, latency, and reliability of AI systems
• Hands-on technical proficiency in Python and SQL, with the depth to design solutions, review the team's work, and make credible architectural decisions
• Familiarity with deployment and MLOps/LLMOps practices (e.g. deployment, monitoring, versioning, and lifecycle management) on at least one major cloud platform (GCP, AWS, or Azure)
• Solid project management capability managing scope, priorities, resources, and timelines across concurrent initiatives, including estimating effort in man-days
• Experience managing or mentoring technical team members, with a genuine interest in developing others
• Excellent communication skills in English, with the ability to move fluently between business and technical conversations
• Professional proficiency in French is strongly preferred
• Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Statistics, or a related quantitative field
• Preferred

Skills

• Experience in a consulting, professional services, or client-delivery environment, including contributing to RFP responses and commercial proposals
• Experience building or scaling a data/AI function or Center of Excellence from an early stage
• Experience with clients, AI governance frameworks, or responsible AI practices
• Relevant cloud, data, or AI certifications (e.g. Google Cloud Professional Machine Learning Engineer or equivalent)
• Working proficiency in Arabic