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.