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.