Senior Engineer - Machine Learning

Key Skills

Aartificial intelligence, Big Data Technology, C++, Continuous Integration & Continuous Delivery - CI/CD, Data science techniques, DevOps, Java Programming, Machine learning techniques, NoSQL, Python Programming, PyTorch, RDBMS, Scala Programming, TensorFlow

Job Description

This position is offered on local terms and conditions. Expatriate assignments and sponsorship for employment visas, even on a time-limited visa status, will not be awarded. This position is for United Airlines Business Services Pvt. Ltd - a wholly owned subsidiary of United Airlines Inc.

United Airlines is an equal opportunity employer. United Airlines recruits, employs, trains, compensates, and promotes regardless of race, religion, color, national origin, gender identity, sexual orientation, physical ability, age, veteran status, and other protected status as required by applicable law.

Qualifications

Required

• Computer Science, Data Science, Generative AI, Engineering or related discipline or Mathematics experience required

• 4 + years of software engineering experience with languages such as Python, Go, Java, Scala, Kotlin, or C/C++

• 3+ years of experience in machine learning, deep learning, and natural language processing.

• 2 + years of experience working in cloud environments (AWS preferred) - Kubernetes, Dockers, ECS and EKS

• 2 + years of experience with Big Data technologies such as Spark, Flink and SQLprogramming

• 2 + years of experience with cloud-native DevOps, CI/CD.

• Strong software engineering experience with Python and at least one additional language such as Go, Java, or C/C++

• Familiarity with data science methodologies and frameworks (e.g., PyTorch, Tensorflow) and experience building and deploying at least one production ML pipeline

• Experience in ML model life cycle development experience and prefer experience to common algorithms like XGBoost, CatBoost, Deep Learning, etc.

• Experience setting up and optimizing data stores (RDBMS/NoSQL) for production use in the ML app context.

• Cloud-native DevOps, CI/CD experience using tools such as Jenkins or AWS CodePipeline; preferably experience with GitOps using tools such as ArgoCD, Flux, or Jenkins X.

• Experience with generative models such as GANs, VAEs, and autoregressive models.

• Prompt engineering: Ability to design and craft prompts that evoke desired responses from LLMs.

• LLM evaluation: Ability to evaluate the performance of LLMs on a variety of tasks, including accuracy, fluency, creativity, and diversity.

• LLM debugging: Ability to identify and fix errors in LLMs, such as bias, factual errors, and logical inconsistencies.

• LLM deployment: Ability to deploy LLMs in production environments and ensure that they are reliable and secure.

• Experience with LLMOps (Large Language Model Operations) to manage the end-to-end lifecycle of large language models.

Preferred

• Computer Science or related STEM field