AI ENGINEER

About the Role

Year of Experience: 6-8 years  Design, build, and deploy production-grade AI applications, copilots, and agentic systems that solve complex business problems.  Develop AI services, orchestration layers, and backend components using Python or Node.js, following modern software engineering and cloud-native principles.  Design and implement Retrieval-Augmented Generation (RAG) architectures, semantic search solutions, vector databases, and enterprise knowledge retrieval systems.  Build and orchestrate intelligent agents using modern agentic frameworks, enabling planning, reasoning, tool usage, multi-step workflows, and task automation.  Apply context engineering techniques to improve AI system performance, including prompt design, retrieval strategies, memory management, grounding mechanisms, and context optimization.  Design and implement integrations using the Model Context Protocol (MCP) and related technologies to enable secure interaction between AI systems, enterprise tools, and external services.  Integrate and operationalize Large Language Models, embedding models, reranking models, and multimodal AI capabilities in production environments.  Develop evaluation frameworks to measure AI quality, relevance, accuracy, latency, safety, and business impact.  Implement AI observability and monitoring, including prompt tracing, response analysis, token consumption tracking, model performance monitoring, and operational dashboards.  Design AI workflows that combine enterprise data, business processes, APIs, and AI services to deliver intelligent automation and decision support.  Collaborate closely with product managers, designers, engineers, and platform teams to identify and deliver high-value AI use cases.  Ensure security, privacy, compliance, and responsible AI practices across the AI lifecycle, including governance, content safety, access control, and prompt injection protections.  Contribute to AI architecture standards, reusable frameworks, engineering best practices, and technical mentorship within the team. Experience & Skills  6-8+ years of software engineering experience with strong expertise in Python or Node.js.  Hands-on experience building and deploying AI-powered applications in production environments.  Experience with agentic frameworks, multi-agent systems, and AI workflow orchestration.  Strong understanding of context engineering, prompt orchestration, grounding techniques, and memory management.  Experience implementing MCP (Model Context Protocol) and AI tool integration patterns.  Strong experience with RAG architectures, vector databases, embeddings, and semantic search.  Experience with Azure AI services, Azure AI Foundry, Azure OpenAI, or equivalent AI platforms.  Understanding of AI evaluation, observability, governance, and responsible AI practices.  Strong communication and collaboration skills with the ability to work across engineering, product, and business teams

What You'll Do

• Year of Experience: 6-8 years
•  Design, build, and deploy production-grade AI applications, copilots, and agentic
• systems that solve complex business problems.
•  Develop AI services, orchestration layers, and backend components using Python
• or Node.js, following modern software engineering and cloud-native principles.
•  Design and implement Retrieval-Augmented Generation (RAG) architectures,
• semantic search solutions, vector databases, and enterprise knowledge retrieval
• systems.
•  Build and orchestrate intelligent agents using modern agentic frameworks,
• enabling planning, reasoning, tool usage, multi-step workflows, and task
• automation.
•  Apply context engineering techniques to improve AI system performance,
• including prompt design, retrieval strategies, memory management, grounding
• mechanisms, and context optimization.
•  Design and implement integrations using the Model Context Protocol (MCP) and

What We're Looking For

• Year of Experience: 6-8 years
•  Design, build, and deploy production-grade AI applications, copilots, and agentic
• systems that solve complex business problems.
•  Develop AI services, orchestration layers, and backend components using Python
• or Node.js, following modern software engineering and cloud-native principles.
•  Design and implement Retrieval-Augmented Generation (RAG) architectures,
• semantic search solutions, vector databases, and enterprise knowledge retrieval
• systems.
•  Build and orchestrate intelligent agents using modern agentic frameworks,
• enabling planning, reasoning, tool usage, multi-step workflows, and task
• automation.
•  Apply context engineering techniques to improve AI system performance,
• including prompt design, retrieval strategies, memory management, grounding
• mechanisms, and context optimization.
•  Design and implement integrations using the Model Context Protocol (MCP) and

Skills

About the Company