Purpose of the role

Design, build and operationalise production-grade Generative AI and Large Language Model (LLM) solutions that unlock business value for Peterson Solutions and its clients—e.g., intelligent document processing, knowledge assistants and automated content generation. The role owns the full solution lifecycle (research → prototype → production), ensures reliability and cost-effectiveness in Azure, and collaborates closely with product, data and DevOps teams to deliver measurable impact.

Responsibilities

• End-to-end ownership of Generative AI features—from feasibility study, model selection and prompt/agent design to production roll-out and monitoring.
• Architect and implement Retrieval-Augmented Generation (RAG) pipelines: data ingestion, embedding creation and vector search.
• Develop prompt schemas, function calling and agent workflows with Semantic Kernel or LangChain.
• Integrate Azure OpenAI, Azure AI Search or other LLM endpoints into the existing micro-service architecture (REST/GraphQL).
• Establish robust MLOps practices: Docker packaging, CI/CD, automated testing, observability dashboards and cost optimisation.
• Track and improve model KPIs (accuracy, latency, hallucination rate) through continuous experimentation and A/B testing.
• Partner with Product Owners to translate business requirements into technical roadmaps and deliverables.
• Produce high-quality technical documentation and conduct knowledge-sharing sessions for engineers and non-technical stakeholders.

Essential

• Bachelor’s degree (or higher) in Computer Science, Software Engineering, Data Science or a related field.
• ≥ 3 years’ experience in machine learning/NLP, including ≥ 1 year hands-on with LLMs or Generative AI.
• Strong coding proficiency in C# or Python / Node.js.
• Demonstrated success in deploying an AI solution to production.
• Deep understanding of LLM architectures, prompt engineering, fine-tuning and parameter-efficient techniques.
• Practical expertise with Semantic Kernel or LangChain and at least one vector database (Azure AI Search, Pinecone, Weaviate, etc.).
• Familiarity with cloud AI stacks—Azure strongly preferred; AWS/GCP equivalents a plus.
• Solid grasp of software-engineering best practices: Git, design patterns, containerisation, Agile delivery.
• Excellent analytical, problem-solving and communication skills; proficient English (spoken & written).

Nice-to-Have

• Experience with Intelligent Document Processing (Azure Form Recognizer, LayoutLM, OpenCV).
• Knowledge of responsible-AI guardrails (content moderation, data privacy, bias mitigation).
• Past deployment of chatbots or knowledge assistants to Microsoft Teams, Slack or web channels.
• Contributions to open-source GenAI projects or published research.