Junior AI/ML Engineer supporting data preprocessing and model evaluation at Node.Digital. Collaborate on AI/Machine Learning efforts with government and commercial clients.
AT
Hybrid Machine Learning Engineer III
Posted last month
About the role
- Machine Learning Engineer at Amex GBT utilizing AI techniques for business travel solutions. Collaborating globally to design, build, and optimize intelligent systems with measurable impact.
Responsibilities
- Utilize AI techniques, including machine learning, deep learning, generative AI, and statistical modeling, to develop and implement solutions for tackling real-world problems such as ranking, chatbot, intent recognition, agentic AI systems, recommender systems, computer vision, NLQ etc.
- Apply strong coding skills, analytical abilities, and innovative thinking to quickly understand new domains and transform creative ideas into functional solutions.
- Employ statistical and data science methods to make data-driven decisions.
- Manipulate large data sets for business insights and drive actionable solutions.
- Utilize appropriate methods and approaches to develop practical solutions for the business.
- Structure work, frame issues, and produce analyses/ML models/Compound AI systems that answer complex business questions in a pragmatic approach.
- Communicate complex data science topics in a clean & simple way to multiple partners and senior leadership.
- Collaborate with a global team across different continents to achieve project goals.
Requirements
- 8+ years of experience with a bachelor’s degree or equivalent, or 5+ years with a master’s degree or equivalent.
- Proven ability to conceptualize business problems and solve them through data science solutions.
- Proven knowledge of AI techniques such as Bayesian methods, Clustering, Ensemble tree models, NLP, etc., with an excellent grasp of statistical concepts and methods.
- Good understanding of LLMs, guardrails, RAG, agentic AI.
- Strong passion for solving problems and finding patterns and insights within structured and unstructured data.
- Industry experience in leveraging AI techniques on real-world large data sets.
- Understanding of the concepts and steps involved in working with real-world large data sets: from domain-specific problem understanding, data preparation, travel-related data processing, feature engineering, data structures for ML, data pipelining, modeling, offline evaluation, online evaluation, and monitoring.
- Strong knowledge of hands-on practice in Python.
- Familiarity with popular machine learning libraries and frameworks such as scikit-learn, Hugging Face, PyTorch and TensorFlow.
- Experience with MLFlow, AWS SageMaker and Bedrock is preferred.
- Familiar with MLOps concept.
- Comfortable with data cleansing using SQL and PySpark.
- Good written and oral communication skills, including the ability to communicate across business areas and increase overall knowledge across the organization.
- Experience with feature stores, machine learning models as service, and monitoring dashboards is a plus.
- Some infrastructure knowledge (AWS, Kubernetes) and cost-awareness are a plus.
Benefits
- Health and welfare insurance plans
- Retirement programs
- Parental leave
- Adoption assistance
- Wellbeing resources
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
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