AI Agent Engineer

Role DescriptionWe are seeking an innovative and highly skilled AI Agent Engineer to design, develop, and deploy intelligent autonomous AI agents that leverage Large Language Models (LLMs), Generative AI, and modern AI orchestration frameworks. The successful candidate will be responsible for building scalable AI agent systems capable of reasoning, planning, tool usage, memory management, and multi-step task execution. This role works closely with software engineers, machine learning engineers, product managers, data scientists, and business stakeholders to develop production-ready AI solutions that automate workflows, improve productivity, and deliver exceptional user experiences.Key responsibilities include designing, developing, and maintaining AI agents powered by Large Language Models (LLMs) for enterprise and customer-facing applications; building autonomous and semi-autonomous agent workflows capable of planning, reasoning, tool invocation, and task execution; implementing Retrieval-Augmented Generation (RAG) architectures using vector databases, embeddings, semantic search, and knowledge retrieval techniques; integrating AI agents with internal systems, APIs, databases, enterprise applications, and third-party services through function calling and agent orchestration; designing prompt engineering strategies, structured outputs, memory management, context optimization, and conversation management techniques; developing multi-agent systems that enable collaboration between specialized AI agents to solve complex business problems; implementing agent evaluation frameworks to measure accuracy, reliability, latency, safety, and overall performance; optimizing inference performance, token usage, scalability, and operational costs for production AI workloads; developing secure, reliable, and observable AI services using cloud-native architectures, microservices, and RESTful APIs; collaborating with engineering teams to implement CI/CD pipelines, automated testing, monitoring, and deployment strategies for AI applications; ensuring AI systems comply with security, privacy, governance, and responsible AI principles; documenting system architecture, technical designs, deployment processes, and operational procedures; researching emerging advancements in agentic AI, multimodal AI, reasoning models, reinforcement learning, and foundation model capabilities; and continuously improving AI agent frameworks, development standards, and engineering best practices.The AI Agent Engineer is expected to combine expertise in software engineering, artificial intelligence, machine learning, and distributed systems to build intelligent, scalable, and production-ready AI agent solutions. Success in this role requires strong problem-solving abilities, technical curiosity, system design expertise, and the ability to translate cutting-edge AI technologies into practical business applications.QualificationsBachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, Data Science, Information Technology, or a related discipline.Strong understanding of Large Language Models (LLMs), Generative AI, Natural Language Processing (NLP), transformer architectures, and AI agent frameworks.Proficiency in Python and experience building production-grade software applications.Experience with AI orchestration frameworks such as LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI, Semantic Kernel, OpenAI Agents SDK, DSPy, Haystack, or similar technologies.Strong knowledge of prompt engineering, Retrieval-Augmented Generation (RAG), embeddings, semantic search, vector databases, and memory architectures.Familiarity with vector databases such as Pinecone, Weaviate, Milvus, Chroma, FAISS, Qdrant, or similar platforms.Experience integrating foundation model APIs and AI services from providers such as OpenAI, Anthropic, Google, Azure AI, AWS Bedrock, or open-source LLMs.Understanding of AI agent planning, reasoning, function calling, workflow orchestration, multi-agent collaboration, and tool integration.Knowledge of RESTful APIs, GraphQL, microservices, distributed systems, and event-driven architectures.Familiarity with cloud platforms including AWS, Microsoft Azure, Google Cloud Platform (GCP), or hybrid cloud environments.Experience with Docker, Kubernetes, CI/CD pipelines, Git, and DevOps best practices.Understanding of software testing, AI evaluation methodologies, observability, monitoring, and performance optimization.Knowledge of SQL, NoSQL databases, data pipelines, and enterprise system integration.Strong analytical, debugging, and problem-solving skills.Excellent written, verbal, and presentation communication skills with the ability to explain complex AI concepts to both technical and non-technical stakeholders.Understanding of AI security, privacy, governance, model evaluation, prompt injection mitigation, responsible AI, and compliance best practices.Professional certifications in Artificial Intelligence, Cloud Computing, Machine Learning, or Software Engineering are considered advantageous but are not mandatory.Demonstrated commitment to continuous learning and staying current with emerging AI agent architectures, reasoning models, multimodal AI, automation frameworks, and Generative AI industry best practices.