Associate AI Engineer
The associate AI Engineer is responsible for delivering value through the delivery of AI solutions to core business functions. This role focuses on applying industry AI techniques, tools, and platforms to develop scalable, production-ready systems that solve real business problems.
As a highly collaborative and delivery-focused contributor, you will support the delivery of our AI initiatives, ensuring measurable impact while working closely with product, wider engineering teams, and business stakeholders. You will develop your technical capabilities as you deliver, applying strong ways of working and contributing to a culture of accountability, trust, and continuous improvement.
This role requires strong fundamental applied AI engineering skills across architecture, software engineering, and MLOps, combined with the ability to communicate effectively, learn continously, and drive business-aligned outcomes.
Contribute to the delivery of AI-enabled features or tasks within defined initiatives with guidance, supporting measurable business value
Deliver against defined acceptance criteria while balancing speed, quality, maintainability, and predictability
Support backlog refinement to translate business problems into applied AI solutions
Work within defined solution architecture aligned with scalability, security, and cost considerations
Work with reference architectures, contributing reusable patterns and improvements
Build and maintain CI/CD pipelines for AI services and supporting infrastructure
Implement MLOps practices for training, deployment, monitoring, and lifecycle management
Ensure solutions meet security, compliance, and operational requirements
Contribute to designing and building production-ready AI systems using modern software engineering practices
Develop clean, maintainable, and well-tested Python code following best practices (TDD, modularity, reuse)
Apply industry solutions such as:
Retrieval-Augmented Generation (RAG)
Document intelligence / document-to-data pipelines
Agentic workflow systems
Integrate AI capabilities into scalable applications and enterprise systems
Support structured experimentation and evaluation methods for AI systems
Support the creation of production evaluation mechanisms to support A/B testing and equivalent
Use metrics to validate performance, quality, and business impact
Continuously improve solutions through feedback, monitoring, and iteration
Clearly communicate technical concepts, trade-offs, and outcomes to both technical and non-technical stakeholders
Participate in demos, design discussions, and cross-functional collaboration
Frame work in terms of business value and measurable outcomes
Actively contribute to a culture of trust, psychological safety, and accountability
Collaborate effectively within squad and across teams to achieve shared outcomes
Collaborate with senior team members to continuously grow your technical and soft skills
Provide and seek constructive feedback to improve team performance
Perform other duties as directed.
Bachelor’s degree in Data Analytics, Data Science, Computer Science, or related field (Master’s preferred).
Foundational experience delivering production software or AI solutions in a team environment
Strong fundamental Python programming skills with focus on production-quality code
Exposure to:
AI/ML systems (LLMs, NLP, or similar)
APIs and system integration
Cloud platforms (preferably Azure)
Understanding of DevOps and CI/CD concepts such as IaC (experience preferred but not required)
Understanding of evaluating AI and probabilistic systems through measurement-based experiments
Familiarity with MLOps pipelines and model lifecycle management
Understanding of scalable architecture for AI solutions
Understanding of responsible AI, security, networking, and compliance considerations
Strong collaboration and teamwork mindset, contributing to shared team success over individual output
Clear and effective communication across diverse teams and stakeholders
Strong collaboration and teamwork mindset, contributing to shared team success over individual output
Proactive mindset with focus on continuous improvement and learning
Ability to balance learning with delivery of assigned responsibilities
Demonstrated accountability and ownership of commitments
Adaptability and openness to change in a fast-evolving AI environment
Strong capability to coordinate and communicate with colleagues across diverse geographic regions, cultures, and time zones.