AI & LLM Development

Design, build, and maintain applications powered by Large Language Models (LLMs).

Implement and optimize prompt engineering strategies to ensure consistent, accurate, and cost-effective model outputs.

Develop and manage RAG pipelines, including document ingestion, embeddings, vector storage, retrieval, and response orchestration.

Evaluate and optimize LLM performance across different use cases and workloads. 

Decision & Rules-Based Systems

Design and implement decision logic using Decision Model and Notation (DMN) frameworks.

Integrate rules-based decisioning with LLM outputs to ensure transparency, control, and explainability.

Collaborate with stakeholders to translate business rules and policies into structured decision models.

 Product & Platform Development

Support the ongoing growth and enhancement of AI-enabled products and platforms.

Collaborate with product managers, designers, and domain experts to translate requirements into technical solutions.

Build APIs, services, and integrations that embed AI capabilities into broader systems.

Ensure solutions are scalable, secure, and production-ready.

Engineering Best Practices

Write clean, maintainable, and well-documented code.

Participate in code reviews, testing, and deployment processes.

Monitor system performance and reliability, and continuously improve solution quality.

Stay current with developments in LLMs, AI tooling, and decision intelligence practices.

-Bachelor’s degree in Computer Science, Software Engineering, Data Science, or a related field, or equivalent practical experience.
-Strong professional experience as a software developer or engineer.
-Demonstrated, hands-on experience with LLM-based systems (non-negotiable).
-Proven experience implementing RAG architectures, prompt engineering, and AI-driven workflows.
-Experience designing or working with DMN or rules-based decision systems.
-Experience contributing to product development in a growing or evolving environment.
-Systems / Tools (Indicative, Not Prescriptive)
 AI & LLM Tooling:-LLM platforms (e.g., OpenAI, Anthropic, open-source LLMs)
-Prompt orchestration frameworks (e.g., LangChain, LlamaIndex, or similar)
-Vector databases (e.g., Pinecone, FAISS, Weaviate)

Decision & Logic Systems:DMN tools and engines
Rules engines and workflow orchestration tools
Engineering & Platform Tools:Backend languages such as Python, JavaScript/TypeScript, Java, or similar
API development (REST / GraphQL)
Cloud platforms (AWS, Azure, GCP)
CI/CD pipelines and version control (Git)