AI Solutions Engineer

Weare seeking an AI Solutions Engineer (Agentic AI) to design, build and deploy enterprise AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) and AI Agents. You will develop production-ready AI applications that automate knowledge-intensive workflows, integrate with enterprise systems and deliver secure, scalable and trustworthy AI experiences. How You will Make An Impact: AI Solution Development • Design, develop and deploy AI agents using LLMs, RAG and prompt engineering. • Build scalable AI workflows that automate enterprise business processes. • Translate business requirements into practical AI solutions. • Develop reusable prompt workflows, tool-calling capabilities and structured outputs. Knowledge & RAG Engineering • Build and optimize RAG pipelines connected to approved enterprise knowledge sources. • Improve retrieval quality through chunking, embeddings, indexing and metadata strategies. • Maintain trusted knowledge bases and ensure source-grounded AI responses. AI Platform & Integration • Integrate AI applications with enterprise systems, APIs, databases and internal platforms. • Develop secure tool-calling capabilities and support deployment into production. • Monitor and optimize AI application performance. Model Quality & Governance • Design evaluation frameworks to measure response quality, retrieval accuracy and hallucination risks. • Optimize prompts, guardrails and model performance. • Support governance, version control and human-in-the-loop review processes. Stakeholder Collaboration • Partner with product, engineering and business teams to deliver AI solutions. • Support demonstrations, UAT, production rollout and technical documentation. • Communicate technical concepts clearly to technical and non-technical stakeholders. Skills for Success: • Bachelor's Degree in Computer Science, Artificial Intelligence, Data Science or related discipline. • 3–5 years of software engineering experience with Python. • Hands-on experience building LLM applications, AI Agents or RAG solutions. • Experience with LangChain, LangGraph, LlamaIndex or similar AI frameworks. • Experience integrating APIs, databases and enterprise systems. • Knowledge of vector databases, semantic search and prompt engineering. • Experience with Git, CI/CD and container technologies. Preferred Skills: • Experience with Azure OpenAI, AWS Bedrock or Google Vertex AI. • Knowledge of MCP (Model Context Protocol) or AI agent orchestration. • Experience deploying open-source LLMs (e.g. vLLM, Ollama). • Exposure to MLOps, modelfine-tuning or domain adaptation.