The posting
About us
Infomineo is a pioneering global AI-enhanced research company that transforms how businesses access, analyze, and act on critical intelligence. We’ve evolved from traditional business research outsourcing to become the strategic partner that combines cutting-edge artificial intelligence with deep human expertise. We offer 3 services to our global clients (leading consulting companies, Fortune 500 companies, and government entities): AI and Data Advisory, Next-Gen Insights and Resource Scaling. This is made possible by relying on 3 pillars of excellence:
- 350+ industry experts spread across 5 offices (Cairo, Casablanca, Mexico City, Dubai, Barcelona).
- Our proprietary AI orchestrator.
- Extensive knowledge assets combining 500,000+ delivered case studies and database subscriptions.
Ready to build the next chapter of AI-driven insights with us?
Why Infomineo? Here's what sets us apart:
- Shape the Data & AI Agenda: You will help define what we build, why it matters, and how it gets delivered at the point where client needs, business functions, and AI capability meet.
- Work with Global Leaders: Our clients are industry leaders — Fortune 500s, top consultancies, governments, and NGOs. You will shape the analytics and AI solutions that directly support their decisions.
- Influence the Technical Direction: You will assess emerging LLM, agentic, and deployment tooling and recommend what earns a place in how Infomineo works, defining delivery standards together with the department and Tech/R&D leadership.
- Step Into Leadership: You will manage, develop, and grow a team of AI engineers, data scientists, and analysts owning their performance, progression, and day-to-day support, and coaching them not only on what to do but on how to think through problems independently.
About this role:
We are seeking an experienced Data & AI Solutions Lead to identify, design, and deliver data analytics and AI solutions with a strong focus on LLM-based and agentic systems across Infomineo's business functions and client engagements.
This is a hybrid leadership role. You will work with stakeholders to uncover where analytics and AI create real value, shape the proposal and business case, and then lead the solution through design, deployment, evaluation, and continuous improvement. You will manage project priorities and resources, stay ahead of developments in generative AI, agentic frameworks, and MLOps/LLMOps, and mentor a growing team of technical specialists.
Success in this role requires equal credibility in three settings: in a room with stakeholders scoping a business problem, in a room with engineers debating architecture and deployment trade-offs, and in a one-on-one developing a member of your team.
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.
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.
What we offer:
- A competitive compensation and benefits package.
- The opportunity to shape and deliver Infomineo's data and AI solution agenda with real global impact.
- A dynamic and supportive work environment that values leadership, innovation, and your contributions.
- Continuous learning and professional development opportunities to propel your career forward in data and AI.
