Crossian ★ 3.8

Tóm tắt công việc
ABOUT THE ROLE
As an Intermediate or Senior AI Engineer, you'll be designing and deploying production-grade AI systems that directly move revenue, shape product strategy, and unlock growth across Global markets. Your models should serve for billions of events, live traffic, and show up in Company-wide KPOs. Beside, from fine-tuning LLMs to crafting recommendation engines, from designing Retrieval-Augmented Generation (RAG) pipelines to launching scalable microservices, you'll work in the core Business Enablement (BE) squad alongside Product, Tech, and Marketing in a fast-paced, Agile playground where experimentation, ownership, and impact rule the day.
If you're ready to jump into real-world AI that delivers tangible outcomes in Global eCommerce, Crossian is your start.
WHAT YOU WILL DO
Model Development & Innovation
Build, train, and tune ML models for recommendations, propensity scoring, fraud detection, and demand forecasting.
Fine-tune and evaluate LLMs (GPT, Llama 3, Claude, etc.) for RAG pipelines, chatbots, and content-generation features.
Prototype quickly, run offline/online experiments, and iterate based on precision, recall, CTR, and business lift.
MLOps & Productionisation
Package models into scalable micro-services with Docker/Kubernetes, SageMaker, Vertex AI, or on-prem GPU clusters.
Automate training, CI/CD, and rollout using GitLab CI, Terraform, and blue/green or canary deployments.
Implement monitoring for drift, latency, and cost, owning the full model lifecycle from commit to sunset.
Data Engineering & Feature Platforms
Partner with Data Engineering to define feature contracts, real-time pipelines (Kafka, Kinesis), and data-quality checks.
Design vector-database schemas (OpenSearch, Pinecone, Chroma) and retrieval logic for RAG-based solutions.
Experimentation & Evaluation
Design online A/B and interleaving tests to measure incremental lift and guard-rail metrics.
Publish clear experiment readouts that influence product roadmaps and marketing budgets.
Cross-Functional Collaboration & Mentorship
Embed in product squads, translating business problems into model specs and success metrics.
Mentor junior engineers and drive best practices in coding standards, documentation, and peer review.
Competitive Compensation: Base salary plus performance bonuses worth up to 30 months of pay per year.
Massive AI Playground: Billions of daily events across text, clickstream, images, and payments ready for experimentation.
Rapid Career Growth: Clear promotion pathways, GPU credits for side projects, conference budget, and mentorship from senior ML leaders.
High-Impact Shipping: Your models serve live traffic, influence eight-digit revenue decisions, and feature in company-wide OKRs.
Ownership Culture: You own your code, experiments, and roadmap and have every opportunity to showcase your capabilities.Essential Qualifications & Skills
Bachelor's degree in Computer Science, Data Science, Electrical Engineering, Statistics, or related field (Master's is a plus).
Experience:
Intermediate: 3+ years of ML engineering.
Senior: 5+ years of ML / AI experience deploying models at scale (e-commerce, ad-tech, fintech, etc.).
Technical Proficiency:
Expert Python skills with ML frameworks (e.g., PyTorch, TensorFlow, LangChain).
Advanced SQL and experience with distributed data processing (e.g., Spark, Dask).
Deep knowledge of recommender systems, classification/regression, NLP, and evaluation metrics (e.g., AUC, F1, NDCG).
Strong software engineering skills, including Git, REST/gRPC APIs, and design patterns.
Hands-on experience with cloud ML platforms (e.g., AWS SageMaker, Google Vertex AI, Azure ML) and container orchestration (e.g., Kubernetes).
Familiarity with Infrastructure-as-Code (e.g., Terraform).
Analytical Rigor: Comfortable with statistical testing, causal inference, and experiment design.
Communication: Ability to translate model outputs into plain-English recommendations for non-technical stakeholders.
Agile DNA: Track record of working in Scrum/Kanban teams and iterative delivery.
Language: Professional English proficiency (written and spoken)
Preferred (Not Required)
Experience building large-scale recommender systems (e.g., deep retrieval, Reinforcement Learning-based ranking).
Familiarity with knowledge graphs or graph neural networks.
Exposure to CUDA optimization, on-device inference, or streaming inference frameworks (e.g., Flink, Ray Serve).
Knowledge of GenAI security practices (e.g., prompt-injection mitigation, compliance frameworks).
Contributions to open-source ML/AI projects, published papers, or patents.