AI Engineer - Agentic AI & Large Language Model Systems

About the Role

We are collaborating with cutting-edge technology companies advancing large language models (LLMs) and agentic AI. This position involves engineering both production-grade AI Agent products for enterprise platforms and contributing to foundational research in LLM training, inference acceleration, and multi-agent systems. This role encompasses a range of AI Engineering responsibilities across applied product development and research-supporting infrastructure.

Responsibilities

• Create and refine AI Agent functionalities including orchestration, tool/function calling, retrieval-augmented generation (RAG), and automated multi-step workflows within core enterprise solutions, partnering closely with Product teams to translate evolving needs into deployable features.
• On the research/infrastructure track, enhance competitive large-language-model capabilities such as long-context reasoning and agentic functionalities, and optimize training and inference through methods like quantization, sparsity, distributed parallelism, and kernel-level tuning. Architect scalable frameworks managing planning, memory, and execution for multi-agent systems.
• Develop evaluation frameworks and instrumentation to continuously monitor and improve agent performance on accuracy, latency, reliability, and cost during production runs.
• Collaborate intensively with Product, Engineering, and Infrastructure divisions; for research-focused roles, contribute to scholarly publications, open-source projects, or patents.
• Analyze production data to detect failure modes, boost reliability, and adapt agent system performance as usage scales up.

Candidate Profile

• Demonstrated experience building or enhancing LLM-driven or agentic systems via academic projects, research, internships, or deployed production solutions.
• Hands-on familiarity with agentic architectures, such as LangChain, LlamaIndex, AutoGen, and experience with RAG pipelines or tool/function-calling frameworks.
• Involvement in large-model training, fine-tuning, or acceleration activities like distributed parallelism, quantization, or sparsity.
• Proven ability to deliver AI-based features from prototype phase through to full production deployment.
• Participation in publication, open-source leadership, or patenting relevant to LLMs, agent systems, or foundational AI infrastructure (preferred for research/infrastructure track).

Required Qualifications and Skills

• Master's degree or higher in Computer Science, AI, Data Science, Electrical/Computer Engineering, or a closely related computational discipline — a strict prerequisite.
• Proficiency in Python as the primary programming language, alongside experience with at least one of Java, Go, TypeScript, or C++.
• Solid practical understanding of LLM concepts including prompting, embeddings, RAG, agent architectures, and tool/function calling, complemented by foundational ML and deep learning knowledge.
• Ability to quickly assimilate new frameworks and vector database technologies independently, with a strong preference for pragmatic problem-solving in unclear or evolving scenarios.
• PhD degree and a strong publication or open-source leadership record are advantageous for research and infrastructure focused applicants.

Opportunity Highlight

This is a chance to engage in shaping innovative agentic AI systems at an early stage, influencing architectural design, infrastructure choices, and the development of AI engineering capabilities as these initiatives expand globally.

Additional Information

If this role aligns with your aspirations, please forward your resume confidentially to the recruiting contact. The company operates under License No. 18S9318 | R1875384.