Full Stack AI Developer

Job Description

• We are looking for a motivated Applied AI Engineer with 3 to 5 years of overall experience to actively engage in the engineering craft, taking a hands-on approach to building and supporting high-visibility projects.
• Your contributions will help deliver solutions that delight customers and users, while driving tangible value for business investments.
• You will apply your growing engineering expertise across multiple programming languages and modern frameworks, consistently delivering quality, outcome-focused work.
• The ideal candidate is a collaborative team player eager to learn, grow, and contribute, working with cross-functional teams to design, develop, and deploy software solutions, while building hands-on experience using AI and agentic tools across the engineering lifecycle.

Responsibilities

•          Contribute to a culture of accountability for customer and business outcomes, helping develop engineering solutions that solve complex problems with valuable outcomes.

•          Support technical design, development, testing, integrations, and ongoing support for assigned product components.

•          Participate in requirement analysis and component-level design under the guidance of senior engineers.

•          Contribute to the integrity of architecture and technical stack to enterprise standards, managing dependencies, code quality, and ongoing maintenance.

•          Stay hands-on, self-driven, and continuously learn new approaches, languages, frameworks, and AI-enabled engineering practices.

•          Write high-quality, supportable, scalable code, and participate in code reviews to support team quality standards.

•          Support rapid, inexpensive experimentation, including AI-built prototypes, to help solve customer needs.

•          Engage with product teams before, during, and after delivery to help ensure the right solution is delivered at the right time.

•          Adopt a mindset that favors action and evidence over extensive planning, helping deliver lean, supportable, and maintainable solutions.

•          Collaborate with cross-functional teams including product management, experience, and delivery to support well-informed decisions.

•          Apply AI and Agentic SSDLC practices to support automated deployments with quality checks embedded across the development lifecycle.

•          Use AI tools for code generation, review, testing, and documentation to accelerate delivery while maintaining quality standards.

•          Build domain-specific knowledge relevant to the business or product, translating business and user needs into technical specifications and code.

•          Communicate clearly with teammates, sharing updates, blockers, and technical trade-offs in a well-structured manner.

•          Engage and collaborate with product engineering teams, building constructive relationships and contributing to a culture of co-creation.

Requirements

•          Bachelor’s degree in Computer Science, Software Engineering, Data Science, Machine Learning, or a related discipline.

•          3 to 5 years of experience with one or more of the following: Angular, React, NodeJS, Python, C#, .NET, Java, SQL/NoSQL, PyTorch, TensorFlow, LangChain, LangGraph, as well as unit testing frameworks.

•          1+ years of experience building AI/ML applications, with hands-on GenAI exposure across LLM integration (OpenAI, Anthropic, or open-source models), RAG pipelines, prompt engineering, or vector databases.

•          1+ years of experience with cloud-native engineering, using FaaS, PaaS, or micro-services on any of the cloud hyperscalers such as Azure, AWS, or Google Cloud Platform, including exposure to their AI/ML services such as Azure OpenAI, AWS Bedrock, or Vertex AI.

•          Working knowledge of engineering standards and best practices, with a willingness to learn and adopt new standards under guidance.

•          Understanding of Business Context Diagrams (BCD), sequence/activity/state/entity relationship/data flow diagrams, OOP/OOD, data structures, and algorithms.

•          Exposure to methodologies and tools such as Agile, DevSecOps, ADO, GitHub, SonarQube, MLflow, or agentic AI frameworks (e.g. LangFuse, LangSmith, or equivalent) is a plus.