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.