Machine Learning Engineer

Role: Machine Learning Engineer

Work location: Bangalore

About Styli

STYLI is an e-commerce marketplace founded in 2019 by Landmark Group, emerging as one of the largest fashion and beauty platforms in the GCC and India. With a strong focus on trendy, affordable fashion and beauty products, STYLI brings over 40,000 styles to men, women, kids, and beauty enthusiasts, offering them the latest global trends delivered directly to their doorsteps.

Our vision is to be the most aspirational value fast fashion and lifestyle destination, delivering seamless service excellence. We aim to create personalized experiences, engaging customers across all touchpoints, and continually expanding our curated selection to meet their evolving needs. STYLI has quickly become a leading player in the e-commerce fashion space across the GCC - Saudi Arabia, UAE, Bahrain, Kuwait and in India.

Role overview

We are looking for a strong and pragmatic Machine Learning Engineer who enjoys turning machine-learning and AI solutions into reliable, scalable production systems.

You will work closely with Data Scientists, Software Engineers, Product Managers and business teams to productionize models, build reusable ML infrastructure and ensure that ML-powered capabilities perform reliably at scale. You should be comfortable operating across software engineering, ML systems and cloud infrastructure, and making practical trade-offs between speed, reliability, performance and cost.

Key responsibilities

• Design, build and maintain production-grade systems for deploying and serving machine-learning and AI models.
• Partner with Data Scientists and Applied Scientists to translate experimental models into robust batch or real-time production workflows.
• Build reusable ML pipelines and tooling for training, evaluation, deployment, monitoring and retraining.
• Develop reliable APIs, services and integration layers for ML-powered product and business capabilities.
• Implement CI/CD, testing, versioning and release practices for ML systems and supporting services.
• Monitor model-serving health, latency, throughput, data quality, drift, failures and infrastructure performance in production.
• Design systems that can scale efficiently while meeting reliability, maintainability, security and cost requirements.
• Work with data and platform teams to ensure dependable feature, training and inference data pipelines.
• Troubleshoot production issues across application, data, model and infrastructure layers.
• Write clean, modular, well-tested, version-controlled and well-documented production code.
• Contribute to architecture discussions, code reviews and engineering standards for ML and AI systems.
• Collaborate effectively with technical and non-technical stakeholders and communicate system trade-offs, risks and operational constraints clearly.

Must-have skills and experience

• Strong proficiency in Python and solid software-engineering fundamentals, including data structures, APIs, testing, version control and modular application design.
• Hands-on experience productionizing machine-learning models in batch and/or real-time environments.
• Experience building backend services or APIs and working with containerized applications and cloud infrastructure.
• Good understanding of the ML lifecycle, including training, evaluation, model packaging, deployment, monitoring and retraining.
• Experience with CI/CD, observability, logging, alerting and production debugging for data-intensive or ML-powered systems.
• Working knowledge of data pipelines, orchestration and relational or cloud data platforms.
• Ability to make sound engineering trade-offs across latency, throughput, scalability, reliability, maintainability and infrastructure cost.
• Strong problem-solving skills and the ability to work effectively with Data Scientists, Engineers and Product teams to take ML solutions from prototype to production.
• A proactive, ownership-oriented mindset with a focus on reliability, operational excellence and continuous improvement.

Good-to-have skills

• Experience with distributed processing, workflow orchestration, feature stores, model registries, model-serving platforms, vector databases, search systems or caching technologies.
• Experience building or operating recommendation, ranking, forecasting, computer vision, multimodal or generative-AI systems in production.
• Familiarity with LLM application infrastructure, including model gateways, retrieval systems, evaluation, guardrails and observability.
• Experience with Kubernetes or managed container platforms, infrastructure-as-code, and modern cloud services on AWS, GCP or Azure.
• Experience in e-commerce, retail, fashion or another large-scale digital product environment.

Education and experience

• Bachelor's or Master's degree in Computer Science, Software Engineering, Data Science, Engineering or a related technical field.
• Typically 3+ years of relevant software engineering, machine-learning engineering or production ML experience; equivalent practical experience will also be considered.

What success looks like

A successful Machine Learning Engineer will turn ML and AI solutions into dependable production capabilities, improve the scalability and reliability of the ML stack, enable faster deployment of models and services, and build reusable foundations that help Data Science and Engineering teams deliver measurable impact.