Key skills

PythonAgentic AI FrameworksLarge Language Models (LLMs)Model Context Protocol (MCP)Retrieval-Augmented Generation (RAG)Prompt EngineeringAI Agent Evaluation and TestingAI GuardrailsCloud Platforms AWSCloud Platforms AzureCloud PlatformsCloud Platforms GCPEnterprise API IntegrationVector DatabasesMLOpsAILLMRAGAgenticCaching

About this role

Xformics Inc. is a global product and strategic consulting company delivering AI and digital products across multiple industry verticals. The AI Engineer will design, develop, and deploy production-ready Agentic AI and LLM-powered solutions, including intelligent agents, RAG systems, MCP-integrated tools, evaluation frameworks, guardrails, and enterprise integrations.

Responsibilities:

Design and build Agentic AI and multi-agent systems incorporating planning, reasoning, routing, memory, state management, and multi-step task execution
Design, develop, and optimize LLM prompts to improve accuracy, reliability, and business outcomes
Build agent workflows incorporating tool/function calling, structured outputs, routing and handoffs, retries, fallbacks, and human-in-the-loop patterns
Develop and integrate MCP (Model Context Protocol) tools and agent-callable tools to connect AI agents with enterprise systems, APIs, databases, and data sources
Design and implement RAG solutions, including chunking, embeddings, vector databases, retrieval, reranking, grounding, and context orchestration
Build evaluation frameworks, evaluation sets, regression tests, and prompt-testing methodologies to measure and improve agent performance, quality, and reliability
Implement guardrails, security controls, observability, tracing, and monitoring for production AI agents
Optimize AI systems for cost and latency through model selection/routing, caching, batching, streaming, asynchronous execution, token management, and context management
Support the deployment, monitoring, troubleshooting, and continuous improvement of AI agents and MCP-enabled workflows
Integrate AI solutions with enterprise APIs, databases, and third-party systems
Collaborate with stakeholders, product managers, architects, and engineering teams to translate business and operational requirements into AI-driven workflows and solutions
Contribute to architecture reviews, technical design, and engineering best practices for AI platforms