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Mid-level AI engineer with broad LLM/cloud requirements and likely metro location increases applicant competition.
Core ML/AI skills are transferable, but aviation/telecom preference raises domain specificity moderately.
Explicit 3–5 year requirement plus mandatory LLM, cloud, and MLOps skills makes shortlisting stringent.
Design, develop, and deploy AI agents and AI-powered applications integrating LLMs, RAG, and agent-based architectures for enterprise use.
Develop machine learning models for prediction, classification, anomaly detection, forecasting, and optimization to solve complex business problems.
Build scalable data pipelines and AI solutions with cloud platform integration and natural language interfaces for enterprise data access.
Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Statistics, or a related field.
3-5 years of experience in Data Science, Machine Learning, Analytics Engineering, or Software Engineering.
Strong programming skills in Python and advanced SQL.
Experience with cloud platforms like AWS, Azure, or Snowflake and AI platforms such as Amazon Bedrock, Anthropic Claude, Azure OpenAI, or OpenAI.
Experienced in building and deploying production AI applications using modern LLMs and Generative AI technologies.
Familiar with AI frameworks like Agentic AI, LangChain, RAG, vector databases, and MLOps including model deployment and monitoring.
Capable of partnering with business stakeholders to identify and deliver impactful AI and data science solutions, including in airline, internet, or telecommunications domains.