Senior AI Solutions Developer

Python | LLMs | Generative AI | AI Agents | AI Assistants | MCP / FastMCP | LangGraph | RAG | APIs | SQL | Databases | LangSmith | W&B | OpenTelemetry | Authentication | Authorization | AI Governance | Responsible AI | HITL | Enterprise Integration Powered by JazzHR. The ideal candidate will have strong hands-on Python and software engineering experience combined with practical expertise in LLMs, RAG, agentic AI, orchestration frameworks, tool integration, and AI observability .

We are seeking an experienced AI/LLM Software Engineer to design, develop, and deploy enterprise-grade AI applications, assistants, agents, and intelligent workflows. The ideal candidate will have strong hands-on Python and software engineering experience combined with practical expertise in LLMs, RAG, agentic AI, orchestration frameworks, tool integration, and AI observability.
This is a highly collaborative, stakeholder-facing role requiring the ability to translate business requirements into secure, scalable, and production-ready AI solutions.
Key Responsibilities

• Design and develop enterprise AI/LLM applications, assistants, agents, and intelligent workflows.
• Build scalable and production-ready solutions using Python and modern software engineering practices.
• Develop agentic AI workflows using frameworks such as LangGraph or similar orchestration technologies.
• Implement tool integrations using MCP/FastMCP or comparable protocols and frameworks.
• Build and integrate REST APIs, enterprise systems, databases, and SQL-based solutions.
• Develop RAG (Retrieval-Augmented Generation) solutions using structured and unstructured data sources.
• Design secure data-access patterns incorporating authentication, authorization, and enterprise security requirements.
• Implement AI evaluation, monitoring, tracing, and observability using tools such as LangSmith, Weights & Biases (W&B), OpenTelemetry, or similar platforms.
• Establish mechanisms to evaluate LLM accuracy, reliability, latency, cost, and overall application performance.
• Incorporate Human-in-the-Loop (HITL) processes into AI workflows where appropriate.
• Apply principles of AI governance, responsible AI, privacy, security, and compliance throughout the development lifecycle.
• Collaborate directly with business stakeholders, product teams, architects, and engineering teams to identify opportunities and deliver AI solutions.
• Act as a forward-deployed engineer, working closely with stakeholders to understand problems, prototype solutions, gather feedback, and rapidly iterate.
• Troubleshoot, optimize, and continuously improve AI applications in production environments.
• Contribute to technical documentation, architecture decisions, development standards, and best practices.

Required Qualifications

• 5–8 years of professional software engineering experience.
• Strong hands-on experience with Python and software development.
• Demonstrated experience building AI/LLM-powered applications in enterprise or production environments.
• Experience developing AI assistants, agents, agentic workflows, or LLM applications.
• Hands-on experience with MCP/FastMCP or similar tool-integration technologies.
• Experience with LangGraph or comparable AI/agent orchestration frameworks.
• Strong understanding of APIs, SQL, databases, and enterprise system integration.
• Experience implementing RAG solutions using structured and/or unstructured data.
• Experience with LLM evaluation, monitoring, tracing, or observability tools such as LangSmith, W&B, OpenTelemetry, or similar.
• Understanding of authentication, authorization, secure data access, and enterprise security practices.
• Strong understanding of AI governance, responsible AI, privacy, security, and Human-in-the-Loop concepts.
• Excellent communication and stakeholder-management skills.
• Ability to work directly with customers/business stakeholders in a forward-deployed engineering capacity.

Preferred Qualifications

• Experience working with major LLM platforms and APIs such as OpenAI, Azure OpenAI, Anthropic, or similar.
• Experience with vector databases, embeddings, semantic search, and retrieval pipelines.
• Experience deploying AI applications in cloud environments such as Azure, AWS, or GCP.
• Familiarity with CI/CD, Git, containers, and modern DevOps practices.
• Experience building enterprise-grade AI solutions with strong emphasis on security, scalability, reliability, and governance.
• Experience working in consulting, professional services, or customer-facing engineering environments.

Key Technologies
Python | LLMs | Generative AI | AI Agents | AI Assistants | MCP / FastMCP | LangGraph | RAG | APIs | SQL | Databases | LangSmith | W&B | OpenTelemetry | Authentication | Authorization | AI Governance | Responsible AI | HITL | Enterprise Integration

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