Senior AI Solutions Developer

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