Lead AI/ML Engineer
4 days ago
ResponsibilitiesAI Strategy Development & LeadershipDefine and lead the overall AI strategy, ensuring alignment with business goals and outcomesPartner with cross-functional teams (Product, Sales, Engineering, Operations) to identify AI-driven opportunities to improve efficiency, accuracy, and speed in workflows across the organizationOperational Efficiency & AutomationImplement AI-driven solutions to reduce the time needed to ramp up Sales and Customer Experience (CX) teams.Leverage AI for workflow automation (e.g., auto-labeling customer issues, optimizing ticket resolution).Collaborate with engineering teams to enhance booking experience for customersRevenue Growth & Customer ExperienceDevelop AI solutions that optimize revenue generation, including personalized recommendations for customers and enhanced itinerary creation.Use AI to drive improvements in customer engagement, reducing friction in the online booking experience.Data Strategy & IntegrationUtilize both relational and non-relational databases to implement robust AI solutions.Ensure effective data governance, security, and scalability as AI becomes a core component of the business.
Ideal Candidate Profile
5+ years of experience in AI/ML roles; Proven track record of implementing AI/ML solutions that have delivered measurable impact on efficiency and revenue.Good understanding of architecture, design and implementation of LLMsExpertise in Python, TensorFlow, PyTorch, or other relevant AI/ML frameworks for data analysis / machine learningExpertise in deep learning algorithms and frameworks such as TensorFlow, PyTorch, or KerasStrong knowledge of NLP techniques and libraries (e.g., NLTK, spaCy, Transformers)Experience with cloud platforms (AWS, Google Cloud, or Azure) for deploying AI solutionsHands-on experience with computer vision libraries (OpenCV, scikit-image)Proficiency in SQL and NoSQL databases (e.g., PostgreSQL, MongoDB, Cassandra)Experience with containerization and orchestration tools (Docker, Kubernetes)Knowledge of MLOps practices and tools (MLflow, Kubeflow, or similar)Understanding of RESTful APIs and microservices architectureKnowledge of recommendation systems and personalization algorithmsUnderstanding of time series analysis and forecasting techniquesExcellent communication skills to articulate AI concepts to both technical and non-technical stakeholders
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