Senior AI Developer

Company Overview

At Bridgenext, we engineer Growth Operating Systems. Most enterprises have spent millions on their revenue stack and still aren't seeing the growth they expected.They have tools that function, but no system that wins. We help growth-hungry companies close that gap by turning fragmented platforms, siloed teams, and disconnected data into one integrated Growth OS. More than a technology company or marketing agency, we're a global digital consultancy with experts in engineering, data, AI, creative and more.

Our teams are made up of experts who believe in engineering impact, starting with putting people at the center of everything we do. Every team member directly shapes our work, culture, and values. Nothing matters more to us than a kind, respectful, fulfilling environment that supports everyone. Our flexible, inclusive culture gives you the autonomy, resources, and opportunities to thrive.

Position Description

The Senior AI Engineer is responsible for designing, developing, and deploying production-grade AI solutions using Generative AI, LLMs, RAG, Agentic AI, MCP, AI-enabled SDLC practices, and Microsoft Azure.

The role partners with architects, technical leads, and business stakeholders to translate requirements into scalable AI applications, develop reusable components, and integrate AI capabilities into enterprise platforms. The engineer ensures solutions meet standards for security, reliability, performance, observability, cost optimization, and Responsible AI, while contributing to engineering best practices, code quality, and continuous improvement.

Key Responsibilities:

• Strong hands-on experience with Azure OpenAI, Microsoft Foundry/Azure AI services, and frameworks such as LangChain/LangGraph, Semantic Kernel, LlamaIndex, or comparable technologies.
• Knowledge of prompt and context engineering, structured outputs, tool/function calling, agent orchestration, memory, and human-in-the-loop workflows.
• Implement MCP servers/clients to securely connect AI agents with enterprise applications, APIs, data, and tools.
• Strong understanding of Microsoft Azure architecture, including Azure AI services, AI Search, API Management, App Services/Functions/Containers, Service Bus, Storage, databases, Key Vault, Entra ID, networking, monitoring, and CI/CD.
• Knowledge of secure, scalable, resilient, observable, and cost-effective cloud architectures using appropriate Azure patterns.
• Strong software engineering experience with Python, C#/.NET, and/or TypeScript/JavaScript, including APIs, microservices, distributed systems, SQL/NoSQL, event-driven architecture, automated testing, Git, and CI/CD.
• Leverage AI-assisted development tools such as GitHub Copilot, Claude Code, OpenAI Codex, or equivalent tools across design, development, testing, debugging, code review, documentation, and modernization.
• Knowledge of AI evaluation practices covering accuracy, groundedness, relevance, hallucination, retrieval quality, safety, latency, and cost.
• Implement AI security and guardrails addressing prompt injection, sensitive-data leakage, insecure tool execution, excessive agency, authentication/authorization, RBAC, and least privilege.
• Knowledge Responsible AI, OWASP LLM security guidance, and NIST AI RMF principles as appropriate.

Must Have Skills:

• 4 to 6 years of experience in software engineering and implementation.
• Strong hands-on experience with Microsoft Azure and enterprise application architecture.
• Demonstrated experience developing Generative AI/LLM solutions, including RAG and agentic AI.
• Experience taking AI solutions from prototype/PoC through production deployment.
• Experience with MCP, agent/tool integration, and enterprise AI integration.
• Hands-on experience using AI coding assistants and development agents throughout the SDLC.
• Bachelor's or Master's degree in Computer Science, Software Engineering, Data Science, Artificial Intelligence, or a related discipline, or equivalent professional experience.

Preferred Skills:

• Microsoft Azure Architecture or AI certifications.
• Experience with enterprise RAG, knowledge architectures, and autonomous/semi-autonomous agents.
• Experience implementing MCP-based enterprise integrations.
• Experience with MLOps/LLMOps and AI evaluation/observability platforms.

Professional Skills:

• Solid written, verbal, and presentation communication skills
• Strong team and individual player
• Maintains composure during all types of situations and is collaborative by nature
• High standards of professionalism, consistently producing high quality results
• Self-sufficient, independent requiring very little supervision or intervention
• Demonstrate flexibility and openness to bring creative solutions to address issues

Bridgenext is an Equal Opportunity Employer