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Why does this job exist and why is it critical?
The Data Scientist - ML & AI Engineer Specialist is responsible for architecting and deploying production-grade AI systems and scalable machine learning pipelines. This role focuses on the end-to-end engineering lifecycle -from advanced model development to ML Ops - ensuring that AI solutions are robust, automated, and seamlessly integrated into the organization's technical ecosystem to drive measurable business impact.
What are you accountable for?
Machine Learning Systems & Architecture: Architect and develop end-to-end ML systems using Python or R. Beyond EDA, focus on building modular, reusable codebases for predictive modeling, recommendation engines, and advanced NLP/Computer Vision architectures.
Production-Grade AI Deployment: Design and implement robust, scalable AI pipelines and microservices. Focus on transitioning models from experimental notebooks to high-availability production environments (Real-time APIs or distributed batch processing) ensuring low-latency and high throughput.
ML Ops & Model Lifecycle Management: Implement automated model monitoring and CI/CD for ML (MLOps). Track performance metrics and data drift in production, ensuring systems are self-healing, scalable, and maintain high reliability under varying load conditions.
Applied AI Research & Innovation: Prototype and integrate state-of-the-art AI advancements - specifically Generative AI, LLMs, and Computer Vision - into existing product stacks to solve domain-specific problems and maintain a technological edge.
Cross-Functional Systems Integration: Partner with business stakeholders to define technical requirements and collaborate deeply with Data Engineers and DevOps to ensure AI solutions are seamlessly integrated into the broader software ecosystem.
Engineering Excellence & Documentation: Champion software engineering best practices within the AI team, including version control (Git), containerization (Docker/Kubernetes), and comprehensive system documentation for reproducibility and technical scalability.
Generative AI Engineering: Architect solutions leveraging Large Language Models (LLMs) through prompt engineering, fine-tuning, and Retrieval-Augmented Generation (RAG) to deliver high-quality, context-aware AI applications.
What do you need to have to fit this role?
A minimum of 4-7 years of professional experience in Data Science or related technical fields, with at least 3 years dedicated to architecting and deploying production-grade ML models and AI pipelines within complex enterprise ecosystems.
Highly proficiency in end-to-end AI development, including Gen AI, Computer Vision, and advanced statistical analysis.
Strong command of SQL, database concepts, and dimensional modeling. Experienced in data transformation methods across various data structures, including relational and unstructured data stores.
Possesses robust analytical and critical thinking skills.
A passionate self-starter who is highly dedicated and capable of working independently.
A strong team player with excellent interpersonal communication skills, proven ability to perform effectively under pressure, and dedicated to delivering results on time.
A background combining Telecommunication industry knowledge with relevant business experience is highly desirable.
What’s next?
Maxis values diverse voices & people. We hire and reward our employees based on capability & performance — regardless of ethnicity, gender, age, education, religion, nationality or physical ability.