Dar, the founding member of the Sidara group, is an international multidisciplinary consulting organization specializing in engineering, architecture, planning, environment, project management, facilities management, and economics. Sidara operates in 60 countries with 20,500 professionals, Dar connects people, places, and communities through innovative solutions to the world's most complex challenges. We deliver projects from inception through completion, embracing challenges to empower communities worldwide. Learn more at www.dar.com.
Our Vision and Values:
We aspire to be the chosen home of those with a gift for crafting solutions that empower people and an unwavering passion for learning and innovation. Our core values shape our culture and guide our decision-making. We are committed to:
- Excellence
- Responsibility
- Empowerment
- Connectivity
- Courage
About the Role:
- We are seeking an experienced AI Engineering & Solutions Manager to lead our Agentic AI and Machine Learning engineering functions and drive the architecture and delivery of enterprise-grade AI solutions across client engagements and internal platforms.
- Reporting to the Head of Technology, you will directly manage the Agentic AI and ML functions and provide senior technical direction across their teams. You will be the primary architectural authority and escalation point for complex AI engineering decisions, balancing hands-on involvement in critical solution architecture, technical evaluations, and problem-solving with leadership, planning, and delivery accountability.
- The role spans LLM-powered applications and agents, Generative AI, machine learning, predictive analytics, and AI platforms. You will partner with the AI Consultancy Lead, Product, Solution Architecture, DevOps, QA, and client stakeholders to translate business needs into secure, scalable, commercially viable, production-ready solutions. This is not a routine coding role, but it requires current technical depth and the ability to contribute directly when needed.
Roles and Responsibilities:
Engineering Leadership & Team Development
- Lead, mentor, and manage, setting clear objectives, accountability, performance expectations, and development plans.
- Build a high-performing AI engineering organization through workforce planning, recruitment, technical interviews, succession planning, coaching, and capability development.
- Coordinate staffing, capacity, priorities, and dependencies across Agentic AI, ML, and shared platform initiatives; remove cross-team impediments.
- Establish a culture of technical excellence, knowledge sharing, ownership, pragmatic innovation, and continuous improvement.
- Own engineering execution, delivery health, technical risks, and quality outcomes across multiple concurrent client and internal initiatives.
AI Solution Architecture & Technical Authority
- Own the overarching architecture direction and reference architectures for enterprise AI solutions across Agentic AI, ML, GenAI, and integrated AI platforms.
- Serve as the senior technical advisor and escalation point for both leads; review and challenge critical designs, technology choices, integration approaches, and architectural trade-offs.
- Remain hands-on in high-impact architecture workshops, proof-of-concepts, technical investigations, design reviews, and resolution of complex engineering issues.
- Guide architectures for agent orchestration, tool integration, multi-agent coordination, retrieval-augmented generation, model serving, predictive analytics, and enterprise data integration.
- Ensure interoperability with enterprise applications, APIs, event-driven services, cloud and on-premises environments, and existing software platforms.
- Evaluate AI models, frameworks, cloud services, and vendors against security, reliability, performance, maintainability, cost, and business requirements.
Engineering Delivery & Production Excellence
- Oversee the end-to-end engineering lifecycle, from technical discovery and architecture through implementation, testing, deployment, and operational handover.
- Guide the leads in defining realistic estimates, delivery plans, technical milestones, acceptance criteria, and production-readiness requirements.
- Champion robust software engineering practices including design and code review, CI/CD, automated testing, infrastructure as code, documentation, and release management.
- Ensure production solutions are observable, scalable, resilient, supportable, and optimized for quality, latency, reliability, and cost.
- Establish meaningful engineering metrics and quality gates, including solution evaluation, model performance, agent task success, operational reliability, and business outcomes.
AI Platforms, Operations & Lifecycle Management
- Provide leadership and governance for MLOps, LLMOps, AgentOps, and AI FinOps practices, working through the respective technical leads and platform teams.
- Oversee model and agent lifecycle management, deployment pipelines, evaluation, monitoring, versioning, rollback, drift detection, and incident response.
- Guide shared AI infrastructure and platform capabilities including model gateways, orchestration services, evaluation frameworks, observability, and reusable engineering accelerators.
- Ensure operational ownership, support processes, service-level expectations, and cost visibility are defined for production AI solutions.
AI Security, Governance & Responsible AI
- Embed security, privacy, and Responsible AI requirements into architecture and delivery decisions from the outset.
- Oversee engineering controls for identity and access management, secrets, data protection, model and agent security, prompt injection, unsafe tool execution, and third-party integration risks.
- Work with AI Consultancy and relevant governance stakeholders to translate applicable policies, regulatory requirements, and Responsible AI standards into enforceable engineering practices.
- Promote auditability, traceability, human oversight, evaluation, and risk-based approval processes across AI solutions.
Stakeholder Engagement & Strategic Collaboration
- Collaborate with the AI Consultancy team on feasibility assessments, client workshops, technical proposals, architecture presentations, and solution estimation while maintaining clear accountability for engineering execution.
- Partner with Product, Solution Architecture, Data Engineering, DevOps, QA, and other engineering teams to align technical decisions with product and client objectives.
- Communicate architectural trade-offs, delivery risks, technical investments, and strategic recommendations clearly to senior management, clients, and non-technical stakeholders.
- Identify opportunities for reusable solutions, platform standardization, innovation, and improved engineering productivity.
Must Have:
- 15+ years of professional experience in software engineering, solution architecture, AI/ML engineering, or related enterprise technology disciplines, including substantial experience in senior technical leadership and engineering management.
- Proven experience managing engineering leads or managers and leading multidisciplinary technical teams, with accountability for architecture, people development, and production delivery.
- Strong, current architectural and practical experience delivering production AI/ML or Generative AI solutions; ability to personally lead complex design reviews, technical investigations, and critical proof-of-concepts.
- Broad understanding of both modern Agentic AI/LLM solutions and traditional ML lifecycles, including data pipelines, training, evaluation, serving, monitoring, and model operations.
- Deep experience with enterprise software architecture, distributed systems, cloud-native design, APIs, integration patterns, security, scalability, and reliability.
- Working knowledge of agent orchestration, tool calling, RAG, vector and hybrid retrieval, model selection, evaluation, and integration protocols such as MCP or equivalent approaches.
- Solid knowledge of MLOps, LLMOps, AgentOps, CI/CD, observability, evaluation, and production operations for AI systems.
- Experience with major cloud and enterprise AI ecosystems, such as Microsoft Azure/Azure AI, AWS, Google Cloud, or equivalent platforms.
- Ability to evaluate engineering trade-offs involving accuracy, latency, security, maintainability, infrastructure, and operational cost.
- Excellent leadership, communication, stakeholder management, and technical decision-making skills, including the ability to engage confidently with executive and client stakeholders.
Good to Have:
- Experience delivering AI solutions across multiple enterprise client engagements or within a technology consultancy.
- Experience across forecasting, predictive analytics, deep learning, computer vision, optimization, and data science in addition to Generative AI.
- Familiarity with Azure AI Foundry, Azure OpenAI, Amazon Bedrock, Google Vertex AI, MLflow, Kubernetes, and modern agent development or observability frameworks.
- Experience in regulated industries, hybrid or sovereign cloud, on-premises AI deployments, and enterprise data governance.
- Exposure to AI governance frameworks, including ISO/IEC 42001, and AI security and Responsible AI implementation.
- Relevant certifications in AI, cloud architecture, enterprise architecture, or engineering leadership.
What we are looking for:
- We are looking for a seasoned engineering leader who combines the judgment of a principal AI architect with the accountability of an engineering manager. You should be comfortable guiding two specialized leads, challenging architectural decisions, and stepping into complex technical problems when necessary, while enabling your teams to own day-to-day implementation.
- Success requires a pragmatic balance of innovation, technical rigor, delivery discipline, and people leadership. You should be able to connect emerging AI capabilities to real business value, build trusted relationships across technical and business teams, and take ownership of engineering outcomes from design through production.
Qualifications:
- Bachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, or a related discipline; a master's degree is advantageous.
- 15+ years of relevant professional technology experience, with demonstrated progression into senior architecture and engineering leadership roles.
- Proven track record of architecting and delivering enterprise-scale production solutions and leading multidisciplinary engineering teams.
** While we carefully review all applications, only candidates meeting the specified requirements will be contacted for further consideration. We appreciate your understanding and thank all applicants for their interest.*
