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

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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