Description Job overview and responsibilitiesUnited Airlines is seeking talented people to join the Data and Machine Learning Engineering team. The organization is responsible for leading data driven insights & innovation to support the Machine Learning needs for commercial and operational projects with a digital focus. This role will frequently collaborate with ML engineers, data scientists and data engineers. This role will design, architect, implement and lead key components of the Machine Learning Platform, Gen AI/ML business use cases, and establish processes and best practices.
- Build high-performance, cloud-native machine learning infrastructure and services to enable rapid innovation across United
- Set up containers and Serverless platform with cloud infrastructure
- You will design and develop tools and apps to enable ML automation using AWS ecosystem
- Build data pipelines to enable ML models for batch and real-time data
- Hands on development expertise of Spark and Flink for both real time and batch applications
- Support large scale model training and serving pipelines in distributed and scalable environment
- Stay aligned with the latest developments in cloud-native and ML ops/engineering and to experiment with and learn new technologies - NumPy, data science packages like sci-kit, microservices architecture
- Optimize, fine-tune generative AI/LLM models to improve performance and accuracy and deploy them
- Evaluate the performance of LLM models, Implement LLMOps processes to manage the end-to-end lifecycle of large language models
- Develop, optimize, fine-tune Generative AI/LLM models to improve performance and accuracy and deploy them
QualificationsWhat's needed to succeed (Minimum Qualifications):- Bachelor's degree in
Computer Science, Data Science, Generative AI, Engineering or related discipline or Mathematics experience required - 5+ years of software engineering experience with languages such as Python, Go, Java, or C/C++
- 5+ years of experience in machine learning, deep learning, and natural language processing
- Strong software engineering experience with Python and at least one additional language such as Go, Java, or C/C++
- Strong technical leadership and familiarity with data science methodologies and frameworks (e.g., PyTorch, Tensorflow) and preferably building and deploying production ML pipelines
- 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) or AgenticOps (Agentic Operations) to manage the end-to-end lifecycle of large language models
- Experience with generative ai methods such as retrieval augmented generation (RAG) and instruction fine tuning
- Must be legally authorized to work in the United States for any employer without sponsorship
- Successful completion of interview required to meet job qualification
- Reliable, punctual attendance is an essential function of the position
What will help you propel from the pack (Preferred Qualifications):- Master's/PhD degree in
Computer Science or related STEM field - 5 + years of experience working in cloud environments (AWS preferred) - Kubernetes, Dockers, ECS and EKS
- 5 + years of experience with Big Data technologies such as Spark, Flink and SQL programming
- 5 + years of experience with cloud-native DevOps, CI/CD
- 3 - 5 + years of relevant enterprise Architecture experience
- 1+ years of experience with Generative AI/LLMs
