Inabia·8 days ago
8 days ago
Senior AI Solutions Developer
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What you'll do
- 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.
What they're looking for
- 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.
Nice to have
- 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.
Summarised by NextRaise from the employer’s description, which follows in full below.
Full description from employer
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
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
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.
- 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.
- 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.
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
Company
Inabia
Rutherford, United States of America
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