Finance Transformation Data/AIML Engineer

Description

This is an onsite role, and you will mainly work from a company office.

The company builds edge-to-cloud products that help customers connect, protect and analyze data and applications.

You join the Digital Transformation and Automation team as an AI and Automation Engineer to help design, build, integrate and deploy enterprise AI and automation solutions in production.

You will partner with the Lead Automation Architect, business teams, data engineering and enterprise technology teams to turn requirements into scalable, secure, production-ready solutions.

This is a hands-on engineering role where you own work from requirements and technical design through testing, deployment and production support.

An internal team lead owns the solution roadmap, architecture approvals, prioritization and business stakeholder relationships, and you work under their direction.

Responsibilities

• Design and build enterprise AI, GenAI, agentic AI, ML and automation solutions.
• Write applications and services in Python using current API and framework tools.
• Build AI agents and workflows that use LLMs, prompt engineering, tool and function calling, RAG and enterprise data.
• Connect solutions to enterprise systems such as Databricks, SailPoint, Okta and Atlas.
• Build and integrate solutions on Azure, including Azure OpenAI and related cloud services.
• Work with Databricks datasets and pipelines for AI, ML and analytics, drawing on knowledge of ML-based forecasting and different ML models.
• Where needed, support integrations with Salesforce, SAP, Anaplan, Power BI, ServiceNow and other business systems.
• Implement authentication and authorization using Okta, Entra ID or Azure AD, OAuth and role-based access controls.
• Run unit, integration and regression tests, and support business UAT.
• Support deployments to development, test/UAT and production environments.
• Troubleshoot issues across applications, integrations, infrastructure and data.
• Apply enterprise standards for security, logging, monitoring, exception handling, source control and deployment.
• Write technical documentation, including solution designs, deployment instructions, support guides and test evidence.
• Work independently and report progress, risks, dependencies and blockers clearly.
• Carry out advanced AI and ML research, track new developments and apply suitable state-of-the-art techniques to hard business problems.
• Design AI solutions for complex problems by analyzing requirements and constraints and proposing suitable ML models and algorithms.
• Weigh scalability, performance and maintainability in every design.
• Mentor junior engineers by sharing best practices, reviewing code and designs, and helping them through technical problems. Take part in technical discussions and contribute thought leadership across the organization.
• Work with product managers, data scientists and business analysts to turn their needs into technical solutions and keep cross-functional projects aligned.
• Drive continuous improvement in AI and ML practice by finding gaps, exploring new techniques and tools, and promoting best practices.
• Evaluate and integrate third-party tools or services that extend AI solution capabilities.
• Run design reviews for your projects to check alignment with requirements and best practices, and mentor junior members during them.
• Work with the engineering manager and team lead to refine designs and implementation approaches, and give peers constructive feedback.
• Organize and take part in meetings to keep team communication effective.
• Prepare and present detailed reports to stakeholders, explaining technical concepts in plain terms for non-technical audiences.
• You may be asked to interpret and report data findings and to maintain or update BI tools, databases, dashboards, systems or methods.
• Where relevant, you may help design and build solutions for complex application, system administration or network problems.

Requirements

• Deep understanding of ML algorithms, including linear regression, decision trees, support vector machines, random forests, neural networks and reinforcement learning.
• Proficient in model selection, hyperparameter tuning and evaluating models with suitable metrics.
• Strong foundations in mathematics and statistics, including linear algebra, calculus, probability and statistical concepts, with the ability to develop complex models and algorithms.
• Proficient in Python, R or Java, with production-level coding, software engineering practice, Git version control and familiarity with development methodologies.
• Knowledge of TensorFlow, PyTorch, sci-kit and Keras is a plus.
• Advanced deep learning knowledge, including CNNs, RNNs, transformers, transfer learning, generative models and deep learning optimization.
• Keep current with AI and ML research, and have experience researching and applying state-of-the-art techniques to complex problems.
• Provide technical leadership and mentorship to junior members, communicate clearly with cross-functional teams and stakeholders, and solve complex technical problems with sound strategic judgment.
• Strong hands-on Python skills.
• Strong understanding of REST APIs and system integration.
• Experience building enterprise applications or automation solutions, including deploying to cloud servers.
• Git, GitHub or equivalent source-control experience.
• Experience with CI/CD and production deployment practices.
• Hands-on experience building GenAI and LLM-based applications.
• Prompt engineering and structured LLM outputs.
• RAG and enterprise knowledge retrieval.
• Understanding of agent, tool and function calling concepts.
• LLM evaluation, guardrails, error handling and hallucination mitigation.
• Experience with Azure OpenAI or comparable enterprise LLM platforms.
• Experience working with Microsoft Azure.
• Experience with agentic AI frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen or equivalent.
• Experience with Azure Container Apps, App Service or Functions.
• Experience with Docker and container-based application deployment.
• 5+ years of overall experience in software engineering, automation, data or AI development.
• 3+ years of strong Python development experience.
• Demonstrable hands-on experience delivering enterprise applications or automation solutions.
• Recent hands-on GenAI and LLM application development is strongly preferred.
• Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, Data Science or a related discipline. Strong equivalent industry experience may also be considered.

Conditions

Primarily onsite at a company office.

Job level TCP03 in the Engineering function.

Base salary range for the United States is $126,500 to $290,500 per year for the Texas location.

Offers may fall outside this range based on location, experience, education and/or skill level.

Variable incentives may be offered on top of base salary.

The company is an equal opportunity employer and makes decisions based on qualifications, merit and business need.

Benefits

• Health and wellbeing benefits covering physical, financial and emotional wellbeing.
• Personal and professional development programs, including support for becoming a subject expert or moving into another division.
• Flexibility to manage work and personal needs.
• US benefit details are published on the company's benefits website.