Data & AI Solutions Lead

Infomineo

. Identify opportunities to apply data analytics and AI services across business functions and client engagements .

Posted 10/7/2026full-timeCasablanca • MoroccoSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in delivering data analytics and AI solutions, with a strong focus on LLM-based systems, project management, and client engagement. Proficient in optimizing AI workloads and ensuring solution quality while mentoring team members and fostering a culture of technical rigor.

Highest-signal resume keywords

Data AnalyticsAI Solution DeliveryLLM-Based SolutionsCI/CD PracticesProject Management

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonSQLMachine LearningData ScienceTechnical Proposal DevelopmentEvaluation Methods for LLM OutputsCost Optimization for AI SystemsMulti-Agent PipelinesRAG SystemsCloud Environment Configuration

Soft Skills

Excellent Communication SkillsMentoringTeam LeadershipStakeholder EngagementProblem-Solving

Tools & Technologies

DockerGCPAWSAzureLangGraphLangChainMLOpsLLMOpsCI/CD PipelinesCloud Platforms

Certifications & Qualifications

Relevant Cloud CertificationsData CertificationsAI Certifications

Industry Keywords

Client-Delivery ExperienceRFP ResponsesCommercial ProposalsAI Governance FrameworksResponsible AI Practices

Tech Stack

Tools & technologies

AWSAzureCloudDockerGoogle Cloud PlatformPythonSQL

About the role

Key responsibilities & impact

Identify opportunities to apply data analytics and AI services across business functions and client engagements
Evaluate business value, feasibility, and priority, translating ambiguous needs into defined problem statements
Develop proposals and RFP/RFI responses covering scope, approach, architecture, staffing, estimates, timelines, and expected impact
Build investment business cases, including trade-offs and risks
Lead client scoping and discovery workshops and manage scope changes and contract amendments
Design custom data and AI solutions, including multi-agent pipelines, RAG systems, and LLM-powered workflows
Own deployment into client and cloud environments, including CI/CD pipelines, containerization, environment setup, and post-deployment monitoring
Define solution quality measures and evaluation loops for LLM and agent outputs
Monitor production performance and drive continuous improvement
Ensure solutions are maintainable, scalable, reliable, privacy-preserving, secure, and responsible
Manage AI workload cost, compute consumption, cloud spend, and latency
Evaluate emerging LLM, agentic, MLOps, and LLMOps technologies and define technical standards
Lead projects end-to-end, coordinating priorities and allocating resources across concurrent initiatives
Communicate progress, risks, dependencies, and trade-offs to leadership, clients, and stakeholders
Plan team capacity and staffing and maintain delivery, estimation, quality, review, and escalation practices
Mentor, coach, and develop team members through training, 1:1s, feedback, objectives, and career planning
Contribute to hiring, onboarding, and retention
Build a culture of technical rigour, intellectual curiosity, and shared ownership
Uphold confidentiality, data protection, information security, company policies, and responsible AI practices

Requirements

What you’ll need

Proven track record in data analytics, data science, or AI solution delivery, typically built over 6 or more years, though readiness is assessed on demonstrated capability rather than years alone
Experience owning delivery end-to-end and guiding the work of others
Experience delivering technical solutions in a client-facing or stakeholder-facing context
Solid working knowledge of DevOps practices, including CI/CD, Docker, cloud environment configuration, and monitoring
Ability to develop and present solution proposals translating business needs into technical approach, scope, and value case
Hands-on experience designing and delivering LLM-based and agentic AI solutions in production, including multi-agent pipelines, RAG, and tool use
Experience with orchestration frameworks such as LangGraph, LangChain, or equivalent
Strong working knowledge of machine learning
Experience defining evaluation methods for LLM and agent outputs and using them to drive improvement
Ability to optimize the cost, latency, and reliability of AI systems
Hands-on technical proficiency in Python and SQL
Familiarity with deployment and MLOps/LLMOps practices on at least one major cloud platform: GCP, AWS, or Azure
Project management capability across scope, priorities, resources, timelines, and man-day estimation
Experience managing or mentoring technical team members
Excellent communication skills in English
Professional proficiency in French strongly preferred
Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Statistics, or a related quantitative field
Preferred: consulting, professional services, or client-delivery experience, including RFP responses and commercial proposals
Preferred: experience building or scaling a data/AI function or Center of Excellence
Preferred: experience with clients, AI governance frameworks, or responsible AI practices
Preferred: relevant cloud, data, or AI certifications
Preferred: working proficiency in Arabic

Benefits

Comp & perks

A competitive compensation and benefits package
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