Applied AI Engineer (LLM/AI Agents)

• Design, develop, and deploy AI-powered assistants (“agents”) to support business functions such as legal, procurement, and construction
• Build and maintain LLM-based applications using external APIs (e.g., OpenAI, Anthropic), ensuring scalability and reliability
• Develop and optimize Retrieval-Augmented Generation (RAG) pipelines for accurate and context-aware document analysis
• Process, structure, and extract insights from unstructured data sources, including contracts, PDFs, Excel files, and emails
• Collaborate with business stakeholders to identify high-impact use cases and translate them into practical AI solutions
• Rapidly prototype and iterate AI tools, delivering working solutions within short development cycles
• Continuously improve AI output quality by reducing hallucinations and enhancing accuracy and consistency
• Integrate AI solutions with internal data pipelines and systems in collaboration with data engineering teams
• Ensure proper documentation, maintainability, and scalability of developed AI applications
• Monitor performance and usage of AI tools, incorporating user feedback to drive improvements and adoption
• Stay up to date with advancements in LLMs and AI tools, and apply relevant innovations to business use cases
• Contribute to establishing best practices, standards, and governance for AI development within the organization

Desired background:

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field
• 2–5 years of experience in software engineering, AI/ML engineering, or applied AI roles
• Proven experience building and deploying real-world AI/LLM applications (beyond academic or tutorial projects)
• Hands-on experience working with large language models (LLMs) and related frameworks (e.g., LangChain, LlamaIndex, or similar)
• Experience working with unstructured and semi-structured data (documents, PDFs, Excel files, emails)
• Strong experience in developing end-to-end solutions, from data processing to application delivery
• Experience integrating APIs and working with external services
• Familiarity with data pipelines and collaboration with data engineering teams
• Exposure to cloud environments (AWS, Azure, or GCP) and modern development practices
• Experience working in fast-paced, delivery-oriented environments with a focus on practical outcomes
• Demonstrated ability to work on ambiguous problems and translate business needs into technical solutions
• Required skills:
• Strong programming skills in Python, with ability to write clean, maintainable, and production-ready code
• Hands-on experience with large language model (LLM) APIs (e.g., OpenAI, Anthropic) and building LLM-based applications
• Solid understanding and practical application of:

Prompt engineering

Retrieval-Augmented Generation (RAG)

Context management and prompt optimization

• Experience working with unstructured and semi-structured data (PDFs, documents, Excel, emails)
• Familiarity with AI/LLM frameworks such as LangChain, LlamaIndex, or similar
• Experience integrating APIs and working with external services
• Ability to design and build end-to-end AI solutions (data ingestion → processing → model → output)
• Understanding of common LLM challenges (e.g., hallucinations, context limitations) and techniques to mitigate them
• Basic knowledge of data pipelines and collaboration with data engineering workflows
• Strong problem-solving skills and ability to work in ambiguous, real-world environments
•  Ability to communicate technical concepts clearly to non-technical stakeholders