Software Engineer evaluating AI - generated code, architecture, and reasoning for Gramian Consultancy. Creating coding problems and feedback to improve next - generation AI models.
About the role
- AI Engineer embedding LLMs, RAG, and automation into KidKare’s configurable childcare SaaS platforms. Accelerating engineering delivery across legacy systems and development teams.
Responsibilities
- Introduce and operationalize AI-assisted development practices, including code generation, AI pair-programming, and automated test generation
- Identify friction in moving requirements from customer requests to shipped features and apply AI tooling to accelerate delivery
- Partner with Angular, React, C#, and TypeScript developers to embed AI tooling into daily workflows
- Build reusable patterns and playbooks to scale AI-assisted delivery across teams
- Design, develop, and deploy AI features into existing configurable SaaS platforms
- Integrate LLMs and ML models into legacy architectures while preserving existing requirements
- Build automation pipelines to reduce manual operational tasks
- Develop intelligent agents and AI-powered workflow systems
- Implement RAG systems and embedding pipelines
- Work with vector databases and retrieval systems
- Build AI orchestration layers and agent frameworks
- Ensure reliability, monitoring, and evaluation of AI outputs
- Deploy and maintain models in production
- Collaborate with engineering leads, product, operators, and non-technical stakeholders
- Translate ambiguous product and development-process problems into scalable AI solutions
- Balance delivery speed with engineering rigor under aggressive customer timelines
- Drive execution with minimal oversight
Requirements
- Experience working within deeply configurable, enterprise-grade SaaS platforms (not greenfield products)
- Strong Python expertise
- Comfortable across Angular, React, C#, and TypeScript
- Backend development experience with APIs and microservices in a mature production codebase
- Experience deploying production systems
- Familiarity with AWS, Azure, or GCP
- CI/CD, Docker, and containerization experience
- Experience integrating LLMs, including OpenAI, Anthropic, or open-source models
- Experience with AI-assisted development tooling at team scale
- Prompt engineering experience
- Experience with RAG pipelines
- Experience with vector databases such as Pinecone, Weaviate, or FAISS
- Experience with embeddings and semantic search
- Familiarity with model fine-tuning
- Familiarity with supervised and unsupervised learning
- Familiarity with evaluation frameworks for LLM systems
- Understanding of hallucination mitigation, guardrails, and output validation
- Experience building data pipelines and ETL processes
- Experience with structured and unstructured data
- Experience with API integrations across SaaS platforms
- Experience with automation tooling and workflow orchestration
- Experience with model versioning and experiment tracking
- Experience monitoring model performance
- Experience with logging and observability
- Experience A/B testing AI features
- Deep comfort working inside legacy, configurable enterprise platforms
- Highly hands-on; able to ship rather than only design
- Comfortable with ambiguity and aggressive timelines
- Business-oriented, with focus on ROI and customer-facing impact
- Pragmatic approach to tool selection
- Experience with CACFP, childcare, child nutrition, education, government programs, or other regulated industries is preferred but not required
Benefits
- No benefits specified in the posting
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