Consultant Apps - (AI + Full stack)

Full Job Description

Collaborate with architects and senior consultants to translate business requirements into technical designs and implementation plans; participate in design reviews and solution walkthroughs. Own feature delivery end‑to‑end: estimate effort, implement features, write unit/integration tests, perform code reviews, and support deployment through CI/CD pipelines. Integrate backend services and data platforms, implement RESTful APIs, and ensure performance, reliability, and observability (logging, metrics, traces) for production readiness. Identify technical risks and dependencies early; raise and help mitigate issues, support technical escalations, and contribute to contingency plans for smooth delivery. Reuse and contribute to shared frameworks and IP, leveraging best practices, accelerators, and patterns to improve delivery speed and predictability. Collaborate cross‑functionally (e.g., project leads, delivery managers, account teams) to articulate technical value, support adoption, and ensure customer outcomes. Continuously learn and improve, adopt modern engineering practices (DevSecOps, automation, testing, cloud monitoring) and sharing learnings with the team. Working knowledge of Python for implementing AI workflows, automation, or AI‑assisted application components. 4-10 years of professional software development experience. Bachelor's degree in computer science, Engineering, or a related discipline, or equivalent practical experience. MCSD/MCAD/MCSE, AZ‑204, AI‑900, AI‑102, or equivalent entry‑ to mid‑level cloud and AI certifications. Full‑Stack Application Development Hands‑on experience designing and building applications using .NET (C#), .NET Web APIs, Node.js with modern front‑end technologies such as JavaScript or TypeScript, and frameworks like Angular or React. Strong understanding of application development across UI, API, service, integration, and data layers. Experience building and consuming RESTful services and integrating backend systems. Working knowledge of relational databases such as Azure SQL, Azure Cosmos DB, PostgreSQL, Azure SQL managed Instance or Azure Database for MySQL, including data access and performance considerations. Solid foundation in software engineering principles, including clean code, debugging, testing, and problem‑solving. Experience with RAG pipelines, vector search workflows, or agent‑based patterns using tools such as Semantic Kernel, LangChain, or similar frameworks. Experience with prompt engineering and optimization to build reliable, high‑quality AI‑driven application workflows. Exposure to evaluation and tuning techniques for improving AI response quality and reliability. Familiarity with microservices and serverless architectures, including Azure Functions, containers, or Kubernetes. Awareness of deployment strategies such as blue/green or canary releases. Exposure to end‑to‑end observability concepts (metrics, traces). Experience with open‑source technologies or familiarity with platforms beyond Azure (e.g., Java, AWS, or Google Cloud Platform). Domain exposure to industries such as financial services, healthcare, manufacturing, or retail. Ability to work closely with senior consultants, architects, and customers to co-create innovative solutions with customers to help solve their business challenges

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