Built on Real-World Finance Experience

Liv Data was built on the idea that finance consulting should be practical. Too often, companies work with a financial advisory firm that delivers recommendations without staying involved through execution. The result is a gap between strategy and reality, where initiatives stall, systems remain underutilized, and finance teams continue to operate under pressure.

Our team comes from hands-on experience across FP&A, accounting, and finance transformation. We’ve worked inside finance teams, not just alongside them. That perspective shapes how we operate today, as a financial advisory firm focused on delivering real business outcomes.

Responsibilities:

• Support client discovery and translate business requirements into technical tasks.

• Build AI assistants, RAG applications, workflow automations and data-driven tools.

• Create rapid prototypes to validate ideas with clients.

• Develop Python services, APIs, data pipelines and system integrations.

• Connect applications with business platforms, databases, APIs, files and cloud services.

• Test AI outputs for accuracy, relevance, hallucinations and failure cases.

• Add appropriate validation, human review and rule-based controls.

• Support application deployment in Azure, AWS or client-managed environments.

• Troubleshoot data, API, authentication and production issues.

• Create technical documentation, test plans and deployment notes.

• Participate in client calls, demonstrations and feedback sessions.

• Provide updates on progress, blockers, risks and changing requirements.

• Support deployed applications through monitoring, maintenance and improvements.

Qualifications:

Required Skills:

• Strong Python programming fundamentals.

• Working knowledge of SQL, REST APIs and data processing.

• Experience building at least one hands-on AI or machine-learning application.

• Understanding of LLMs, prompt design, embeddings, vector databases and RAG.

• Ability to explain an application's architecture and personal contribution clearly.

• Familiarity with Git, testing, debugging and software-development practices.

• Understanding of how applications move from local development to deployment.

• Strong problem-solving and analytical skills.

• Clear written and verbal communication.

• Ability to work independently in a small, fast-moving team.

Preferred Experience:

• Azure or AWS application deployment.

• Flask, FastAPI or a similar Python backend framework.

• Azure OpenAI, OpenAI, Anthropic, Amazon Bedrock or similar platforms.

• Power BI, Microsoft Fabric or other analytics platforms.

• Databases and vector stores such as PostgreSQL, SQL Server, FAISS or Pinecone.

• Authentication, role-based access and secure credential management.

• Integrations with ERP, CRM, e-commerce or project-management systems.

• Logging, monitoring, CI/CD and production support.

• Data governance, AI guardrails and responsible-AI practices.

Education and Experience:

• Bachelor's or master's degree in Computer Science, Data Science, Artificial Intelligence, Engineering or a

related field.

• Approximately 0–3 years of relevant professional, internship or strong project experience.

• Practical experience and demonstrated problem-solving ability will be valued alongside formal qualifications.

What We Look For:

• Evidence of tools or applications personally built and delivered.

• A problem-first approach — not using AI simply because it is available.

• Ability to decide when to use AI, automation, analytics or fixed rules.

• Comfort working with incomplete requirements and changing priorities.

• Willingness to communicate directly with clients and receive feedback.

• Ownership of work from initial problem through deployment and support.

• Curiosity, adaptability and a willingness to learn unfamiliar platforms

To Apply:

Send your resume and a short note on an AI, data or automation tool you personally built — what the problem

was, what you shipped, and what it changed — to [email protected]