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 a Senior Agentic AI Solutions Engineer to join our growing practice delivering enterprise AI solutions to clients across industries. This is a hands-on senior engineering role focused on designing, building, deploying, and operating production-grade Agentic AI systems.
The majority of your work will focus on client engagements, translating business requirements into secure, scalable, and production-ready AI solutions. You will work closely with the Lead Agentic AI Solutions Engineer, solution architects, product owners, clients, and engineering teams, taking substantial ownership of assigned technical components and solutions from implementation through production support. You will also contribute to reusable accelerators, internal AI platforms, and engineering practices.
You should bring strong software engineering fundamentals, demonstrable experience with production Agentic AI systems, sound technical judgment, and the ability to collaborate effectively with technical and business stakeholders.
Roles and Responsibilities:
Senior Engineering Contribution & Collaboration
- Independently deliver complex engineering workstreams and contribute to technical design decisions within the agreed solution architecture.
- Collaborate with the Lead and other engineers to translate architectural designs into maintainable, production-ready implementations.
- Participate in code reviews, design reviews, technical troubleshooting, and continuous improvement of engineering quality.
- Mentor junior engineers through technical guidance, pairing, and constructive code-review feedback.
- Contribute to reusable components, implementation patterns, documentation, and engineering accelerators.
- Support client workshops, technical demonstrations, solution discussions, and estimation activities as required.
Agentic AI Solution Engineering & Delivery
- Design, develop, deploy, and maintain production-grade Agentic AI applications and services for assigned client solutions.
- Implement intelligent agents with planning, workflow orchestration, structured decision-making, tool calling, and memory capabilities.
- Build multi-agent workflows supporting collaboration, delegation, and coordination using A2A or equivalent approaches where appropriate.
- Develop secure enterprise tool and system integrations using MCP or equivalent integration patterns.
- Implement enterprise RAG pipelines including document ingestion, chunking, embeddings, hybrid retrieval, reranking, vector search, and graph-based retrieval where suitable.
- Develop backend services, APIs, orchestration components, and event-driven integrations connecting AI capabilities to enterprise platforms.
- Evaluate models, frameworks, and implementation approaches; communicate trade-offs involving accuracy, latency, security, maintainability, and cost.
Production Engineering Excellence
- Build reliable, testable, maintainable services suitable for enterprise production environments.
- Apply distributed systems fundamentals including asynchronous processing, messaging, caching, APIs, and event-driven patterns.
- Instrument solutions with tracing, telemetry, prompt and token monitoring, latency measurements, cost analytics, and operational dashboards.
- Implement automated and human-in-the-loop evaluations covering response quality, task completion, hallucinations, retrieval performance, and reliability.
- Investigate production issues, diagnose root causes, and improve solution quality, performance, resilience, and operational efficiency.
- Apply CI/CD, automated testing, containerization, documentation, release practices, and deployment controls in coordination with platform and DevOps teams.
Security & Responsible AI
- Implement security, privacy, and governance requirements defined for each solution.
- Apply secure authentication, authorization, identity integration, and secrets management practices.
- Implement safeguards against prompt injection, data leakage, unauthorized tool execution, insecure integrations, and model misuse.
- Support explainability, transparency, human oversight, auditability, evaluation, and ongoing monitoring in deployed AI solutions.
- Work with technical leads and relevant stakeholders to meet organizational, regulatory, and client security requirements.
Must Have:
- 7+ years of professional software engineering experience in backend engineering, distributed systems, platform engineering, or related disciplines.
- Minimum 2 years of hands-on experience building and deploying production Agentic AI systems, with concrete examples of personal contributions, technical decisions, and outcomes.
- Strong experience developing enterprise applications using APIs, distributed services, asynchronous processing, messaging, caching, and cloud-native patterns.
- Strong understanding of Agentic AI concepts including planning, workflow orchestration, tool calling, memory, multi-agent workflows, and agent orchestration.
- Hands-on experience with MCP or equivalent agent integration patterns and familiarity with A2A or equivalent multi-agent coordination approaches.
- Practical experience with at least one modern Agentic AI framework.
- Experience with enterprise AI platforms such as Azure AI Foundry, Azure OpenAI, AWS Bedrock, Google Vertex AI, or equivalent.
- Strong Python development experience; Java, C#, or Node.js experience is advantageous.
- Hands-on RAG implementation experience including embeddings, semantic and hybrid search, reranking, vector databases, chunking, and document ingestion.
- Experience implementing AI evaluations using automated benchmarks, human review, or model-based evaluation.
- Experience with AI observability or LLMOps tools such as LangSmith, Langfuse, Arize Phoenix, or equivalent.
- Working knowledge of Git, Docker, Kubernetes fundamentals, CI/CD, monitoring, and cloud deployment practices.
Good to Have:
- Experience mentoring junior engineers or serving as a senior technical contributor on client engagements.
- Experience with knowledge graphs, GraphRAG, graph-based retrieval, and advanced semantic ranking.
- Experience delivering AI solutions in regulated environments, sovereign cloud, or on-premises deployments.
- Experience with Kubernetes, Infrastructure as Code, and production operations.
- Familiarity with MLOps, LLMOps, model lifecycle management, and AI governance tooling.
- Experience with event streaming, message brokers, and complex enterprise integration patterns.
- Relevant certifications in AI, cloud platforms, or software engineering.
What we are looking for:
We are looking for a strong hands-on engineer who can independently solve complex technical problems and deliver reliable enterprise Agentic AI solutions. You should be comfortable implementing distributed services, integrating AI into enterprise environments, reviewing code, collaborating on architectural decisions, and explaining technical trade-offs.
Success in this role requires consistent delivery ownership, attention to quality and security, effective collaboration with technical leads and clients, and a pragmatic approach to balancing engineering excellence with project timelines. Experience contributing to multiple enterprise client engagements in agile environments is highly desirable.
Qualifications:
- Bachelor’s degree (or equivalent experience) in Computer Science, Software Engineering, or a related discipline.
- 7+ years of professional software engineering experience.
- Minimum 2 years of proven hands-on experience building, deploying, and supporting production Agentic AI systems.
* 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
