Senior Consultant - AI Software Engineer- AI Hub

Overview

Core Requirements

• 5+ years of professional experience delivering enterprise-grade software solutions across backend and frontend systems.
• Strong hands-on experience building secure, scalable, and maintainable APIs using Python and Node.js, with frameworks such as FastAPI and Fastify.
• Solid frontend engineering experience with TypeScript, React, and Next.js, following enterprise UI and accessibility standards.
• Proven experience containerizing, deploying, and operating applications in production using Docker and Kubernetes, with a strong understanding of reliability and scalability.
• Experience designing, implementing, and maintaining CI/CD pipelines using Jenkins, GitHub Actions, or Azure DevOps, with automated testing and quality gates.
• Hands-on experience deploying and operating production workloads on Microsoft Azure, including services such as AKS, Azure Container Apps, Azure Container Registry, and related platform services.
• Strong understanding and application of software engineering best practices, including SOLID principles, clean architecture, and common enterprise design patterns.
• Experience working with relational databases such as MS SQL Server and PostgreSQL, including schema design, query optimization, and transactional consistency.
• Practical experience with vector databases (e.g., Chroma, Milvus, Pinecone) and pgvector, including building and operating Retrieval Augmented Generation (RAG) pipelines in production environments.
• Ability to collaborate effectively in cross-functional enterprise teams, including product, architecture, security, and cloud/platform teams.
• Experience working with LLMs and AI platforms, such as Azure OpenAI, OpenAI APIs, or similar enterprise AI services.
• Familiarity with LLM orchestration frameworks such as LangChain, Semantic Kernel, or equivalent.
• hands-on experience using AI coding assistants (Claude Code, OpenAI Codex,..etc)
• Strong documentation skills and experience contributing to architecture decisions, technical standards, and internal best practices.
• Prior experience mentoring junior engineers or contributing to engineering excellence initiatives.
• Nice to Have
• Exposure to MLOps or AI platform operations, including model lifecycle management, monitoring, and observability.
• Experience with event-driven or distributed architecture (e.g., message queues, service buses, async processing).
• Knowledge of observability and monitoring tools (e.g., Azure Monitor, Application Insights, Prometheus, Grafana).
• Experience working in regulated or compliance-driven environments (e.g., financial services, audit).
• Familiarity with infrastructure-as-code tools such as Terraform, Bicep, or ARM templates.