Agentic AI Engineer

Company Overview:

Arctiq is a global, intelligence-driven technology services company delivering professional and managed services across Hybrid Cloud Infrastructure, Networking & Connected Experiences, Cybersecurity, Data & AI, Autonomous Operations & Intelligence, and Enterprise Service Management. We help organizations operate, secure, and modernize complex environments by unifying infrastructure, networking, data, security, automation, and observability under a single, integrated operating model. Our work focuses on helping customers reduce operational friction, improve resilience, and make better, faster decisions as their environments evolve.

Arctiq builds on decades of industry expertise and a customer-centric ethos to deliver exceptional value to clients across diverse industries.

This is a remote, contract opportunity for one of Arctiq's clients. This team works EST hours.

Position Overview:

We are seeking a highly skilled Agentic AI Engineer to lead the design, development, deployment, and support of next-generation intelligent automation solutions. This role is focused on building production-grade AI agents and agentic workflows that automate complex business processes, integrate with enterprise systems, and deliver measurable business outcomes.

Working closely with business stakeholders, architects, data teams, and developers, you will identify opportunities where AI agents can improve operational efficiency, accelerate decision-making, and transform business processes. You will be responsible for taking AI solutions from concept through production, ensuring they are scalable, secure, observable, and maintainable.

Responsibilities:

• Design, develop, deploy, and maintain Agentic AI solutions that automate business processes and complex workflows.
• Build autonomous and semi-autonomous AI agents capable of reasoning, planning, tool usage, retrieval, memory management, and multi-step task execution.
• Develop and deploy multi-agent systems that collaborate to accomplish business objectives.
• Integrate Large Language Models (LLMs) from providers such as OpenAI, Anthropic, Google, Azure OpenAI, and open-source alternatives.
• Build Retrieval-Augmented Generation (RAG) solutions leveraging vector databases and enterprise knowledge sources.
• Design and implement agent orchestration frameworks using technologies such as Lang Graph, Lang Chain, CrewAI, Auto Gen, Semantic Kernel, Llama Index, or similar platforms.
• Develop APIs, microservices, and backend services that support AI-driven applications and workflows.
• Integrate AI agents with enterprise systems including CRM, ERP, ticketing platforms, collaboration tools, databases, and third-party SaaS applications.
• Implement robust observability, monitoring, evaluation, logging, guardrails, and governance frameworks for AI solutions.
• Collaborate with business stakeholders to identify automation opportunities and determine where AI agents can deliver measurable value.
• Evaluate emerging AI technologies and recommend practical approaches for implementation.
• Perform hands-on development using Agile and Scrum methodologies.
• Continuously optimize deployed AI systems for performance, reliability, cost efficiency, and user adoption.
• Troubleshoot production AI applications and rapidly resolve operational issues.
• Serve as a technical advisor on AI automation strategy, architecture, and implementation best practices.

Qualifications:

• 5+ years of software engineering experience with modern application development.
• 1+ years of hands-on experience building and deploying Generative AI or Agentic AI solutions in production environments.
• Demonstrated experience designing, developing, and deploying AI agents that interact with APIs, databases, enterprise systems, and external tools.
• Proven experience building autonomous workflows using agent orchestration frameworks such as Lang Graph, Lang Chain, CrewAI, Auto Gen, Semantic Kernel, Llama Index, or equivalent technologies.
• Strong Python development experience and familiarity with AI application development ecosystems.
• Experience integrating commercial and open-source LLMs into production applications.
• Strong understanding of prompt engineering, agent design patterns, tool calling, memory management, context management, and AI workflow orchestration.
• Experience implementing Retrieval-Augmented Generation (RAG) architectures and vector database solutions.
• Experience working with cloud platforms such as Azure, AWS, or GCP.
• Experience with Docker, containerized deployments, CI/CD pipelines, and modern Dev Ops practices.
• Ability to interpret SDK documentation and rapidly implement integrations with new platforms and services.
• Excellent problem-solving skills with the ability to decompose complex business challenges into scalable AI-driven solutions.
• Strong communication skills with the ability to explain technical concepts to both technical and non-technical audiences.

• Experience deploying AI solutions on Azure OpenAI, AWS Bedrock, Vertex AI, or similar enterprise AI…