Description :

- Reporting to the Senior AI Consultant, the AI Engineer will design, build, deploy, and operate AI systems across Unifi Services enterprise platforms.

Core Responsibilities :

- Build and integrate AI-driven capabilities into enterprise applications and workflows using leading LLM platforms

- Design and implement Retrieval-Augmented Generation (RAG) architectures to enable AI systems to securely leverage internal knowledge and data sources

- Develop and orchestrate AI agents capable of executing multi-step, decision-based business processes

- Own production AI systems end-to-end, including deployment, versioning, monitoring, scaling, and cost optimization.

- Define, implement, and maintain evaluation metrics for AI quality, reliability, latency, and cost efficiency.

- Collaborate closely with Leadership, Product and Engineering teams to identify, prioritize, and deliver high-impact AI solutions.

- Ensure AI solutions follow enterprise standards for security, data privacy, reliability, and maintainability.

Must-Have (Non-Negotiable) Skills & Experience :

- 5 to 7 years of overall software engineering experience, with 3 to 4 years of hands-on work on production AI / LLM systems.

- Strong proficiency in Python, with experience writing production-quality, testable, and maintainable code.

- Proven experience designing and implementing RAG pipelines, including document ingestion, embeddings, retrieval, and response generation.

- Hands-on experience with vector databases (e.g., Qdrant, FAISS, ChromaDb, or similar).

- Practical experience using LLM orchestration frameworks such as LangChain, LlamaIndex, Autogen, Haystack, or Semantic Kernel.

- Prior experience building AI solutions in SaaS or enterprise-scale software environments.

- Experience integrating with major LLM providers such as OpenAI, Anthropic Claude, or Google Gemini.

- Solid understanding of prompt engineering, context engineering (context window management), and output control techniques.

- Experience deploying AI systems to cloud environments (AWS, Azure, or GCP) using Docker and Kubernetes.

- Working knowledge of LLMOps & MLOps practices, including model versioning, CI/CD, monitoring, and rollback strategies.

- Experience in implementing guardrails in AI Solutions along with Observability.

Preferred / Nice-to-Have Qualifications :

- Exposure to multimodal AI systems, model fine-tuning, or reinforcement learning from human feedback (RLHF).

- Familiarity with Model Context Protocol (MCP).

- Understanding of AI cost optimization, latency tuning, and performance benchmarking in production.

- Experience with domains such as eCommerce, Retail, HR, Finance, Legal, Compliance, etc

Language & Documentation Expectations :

- All production AI code must be clearly documented, version-controlled, and supported by appropriate tests.

- AI pipelines, prompts, and agent workflows must include design documentation and usage guidelines.

- Each production AI system must have defined ownership, monitoring dashboards, and operational runbooks.

- Clear documentation of model limitations, assumptions, and fallback behaviours is mandatory.