AI Engineer
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Job Description
Lead AI Engineer at Arctiq developing intelligent automation solutions with AI agents to streamline business processes. Work requires collaboration with stakeholders to deliver measurable outcomes.
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 LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel, LlamaIndex, 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.
Requirements:
- 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 LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel, LlamaIndex, 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 DevOps 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 platforms.
- Experience with MCP (Model Context Protocol) implementations and AI tool ecosystems.
- Bachelor's degree in Computer Science, Software Engineering, MIS, or related field.
Benefits:
- Flexible work arrangements
- Professional development
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