New opportunity

Senior AI and ML Engineer

Delta Air Lines, Inc. · Bangalore, India

About this role

Role Summary: Join Delta IT as we leverage AI to unlock powerful customer experiences and refine airline operations. As a Senior AI and ML Engineer, you will work on initiatives ranging from complex global operations to digital customer experiences, utilizing techniques such as Generative AI, deep learning, machine learning, reinforcement learning, and CNNs. Responsibilities: Grow Delta’s use of AI in customer and employee-facing experiences; partner with cross-functional teams to drive AI opportunities; provide technical thought-leadership; design and build AI components, APIs, and loosely coupled applications at scale; incorporate security standards, test-driven development, and pipeline automation; mentor junior team members; and monitor metrics, trends, and costs to improve efficiency. Qualifications: Bachelor’s degree in CS, IT, Engineering, or related field; 4-7 years of work/educational experience in AI/ML; experience with Agile, DevOps/DevSecOps, AWS, and AWS Sagemaker; proficiency in Python, R, Scala, or Spark; experience with NLP, classification, and pattern recognition; familiarity with AI frameworks (TensorFlow, Keras, PyTorch, etc.), GenAI technology stacks, and VectorDBs; strong foundation in statistical modeling and model evaluation; and demonstrated ability to communicate complex analytical findings to diverse audiences. Preferred: A self-motivated 'doer' with a bias for action, strong ownership, curiosity about passenger pain-points, and a passion for travel. Benefits: Medical insurance, transportation, cafeteria meals, travel privileges, car lease program, paid time off, parental benefits, and national pension scheme.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

PythonRScalaSparkAWSAWS SagemakerTensorFlowMicrosoft CNTKscikit-learnKerasCaffeGluonPyTorchNatural Language ProcessingGenerative AIDeep LearningReinforcement LearningCNNsVectorDBsPrompt EngineeringLLM fine-tuningAgileDevOpsDevSecOpsStatistical Modeling

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Production-Grade AI Deployment
The role requires designing, building, and scaling AI components and APIs within a production environment. You must demonstrate the ability to move beyond model accuracy to ensure reliability, latency management, and scalability, while integrating these components into highly available airline systems.
Cross-Functional Business Alignment
You must partner with diverse teams to identify AI opportunities that solve real-world operational and customer experience problems. Success depends on your ability to translate complex analytical findings into business terms for non-technical stakeholders and operational leaders.
Robust Engineering Standards
The position mandates the incorporation of security standards, test-driven development, and pipeline automation. You are expected to own the full lifecycle of your solutions, including production issue triage and continuous improvement of system performance and stability.
Technical Leadership and Mentorship
As a senior engineer, you will provide thought leadership on AI frameworks and design patterns. You are responsible for mentoring junior team members, leading code reviews, and fostering an AI-first mindset across the organization.

Likely challenges to think through

Balancing Model Performance with Operational Costs
You will be evaluated on business outcomes rather than just model metrics. Prepare to discuss how you manage trade-offs between precision/recall and the cost of false positives/negatives, specifically in the context of high-stakes airline operations where latency and reliability are critical.
Navigating Complex Legacy and Distributed Environments
Delta operates in a complex, global landscape. You may face difficulty integrating modern AI solutions with existing, highly available legacy systems. Prepare by highlighting your experience with loosely coupled architectures and your strategy for ensuring seamless integration without disrupting core operations.
Ambiguity in Problem Definition
The role involves solving problems where the optimal analytical approach is not known in advance. Prepare for this by describing your process for data exploration, hypothesis testing, and how you pivot your technical approach when initial models fail to meet business requirements.

Practice your answers

Technical practice

Model Evaluation Beyond Accuracy

What to coverDiscuss how you evaluate models using operational outcomes rather than just accuracy. Address the trade-offs between precision and recall, the business costs of false positives versus false negatives, and how latency and scalability impact production deployment.

Generative AI and Vector Databases

What to coverExplain your experience with the GenAI technology stack, including prompt engineering and LLM fine-tuning. Describe how you utilize VectorDBs to support production-grade generative AI applications and training pipelines.

Designing Scalable AI Components

What to coverDescribe your approach to designing and building loosely coupled AI components and APIs at scale. Focus on how you integrate these components into highly available systems while maintaining performance and reliability.

DevSecOps and Pipeline Automation

What to coverDetail how you incorporate security standards and automated deployment pipelines into your machine learning workflows. Explain your experience with test-driven development for AI models and how you ensure robust reliability in production.

Handling Unknown Analytical Approaches

What to coverProvide an example of a project where the optimal analytical approach was not known in advance. Explain how you combined domain knowledge, data exploration, and machine learning to identify and solve the operational problem.

AWS Sagemaker and Cloud Infrastructure

What to coverDiscuss your experience using AWS and AWS Sagemaker for end-to-end machine learning lifecycles. Focus on how you manage resources and costs while monitoring metrics to improve efficiency.

Statistical Modeling and Pattern Recognition

What to coverExplain your foundation in statistical modeling, including supervised and unsupervised learning, anomaly detection, and forecasting. Discuss how you apply these techniques to real-world data to drive business insights.

Mentorship and Code Quality

What to coverDescribe your process for leading code reviews and mentoring junior team members. Explain how you promote best practices for documentation, error logging, and tracking to ensure long-term maintainability of AI products.

Behavioural practice

Communicating Complex Findings

What to coverDescribe a time you had to explain complex analytical findings to non-technical stakeholders or executives. Focus on how you translated technical details into understandable business terms to drive decision-making.

Cross-Functional Collaboration

What to coverShare an experience where you partnered with cross-functional teams, such as Architecture or Security, to drive an AI initiative. Explain how you built trust and navigated differing priorities to achieve a successful outcome.

Ownership and Continuous Improvement

What to coverProvide an example of a production issue you triaged or a process you improved. Explain how you took ownership of the solution and what steps you took to prevent future occurrences.

Empathy for Customer Pain-Points

What to coverDescribe a situation where you identified a user or customer pain-point and developed a technical solution to address it. Explain how you balanced technical feasibility with the need to create a meaningful user experience.

Adapting to New Information

What to coverDiscuss a time you held a strong opinion on a technical approach but changed it in the face of new facts. Explain how you handled the pivot and ensured the team remained aligned.

Sharpen your knowledge

Topics to prepare

Model Evaluation Beyond Accuracy
The role requires evaluating models based on business outcomes rather than just accuracy. Revise concepts like precision-recall tradeoffs, false-positive/false-negative costs, and the impact of latency and reliability on operational efficiency. This is critical for aligning AI solutions with Delta's operational goals.
Generative AI and LLM Infrastructure
Review the GenAI technology stack, specifically focusing on prompt engineering, guardrails, and LLM fine-tuning. Understand how VectorDBs integrate into training pipelines and production applications to support large-scale AI initiatives.
Production-Grade AI Engineering
Focus on the lifecycle of AI components in production, including test-driven development, pipeline automation, and monitoring metrics. Study how to design loosely coupled applications that integrate with highly available systems, as this is central to the role's responsibilities.

Skills to revise

AWS Sagemaker Deployment
Practice building and deploying machine learning models using AWS Sagemaker. Focus on creating automated training and deployment pipelines, as this is a core requirement for the role's infrastructure and DevOps responsibilities.
Python for Data Science and AI
Refine your proficiency in Python, specifically using libraries like scikit-learn, TensorFlow, or PyTorch. Practice implementing classification, clustering, and anomaly detection algorithms to demonstrate your ability to solve real-world operational problems.
Technical Communication for Diverse Audiences
Prepare to explain complex analytical findings to non-technical stakeholders. Exercise by drafting a summary of a past project that highlights the business impact, operational efficiency gains, and cost implications rather than just the technical methodology.

Your preparation checklist

Prepare a project walkthrough focused on production-grade AI
Select a past project where you deployed an AI/ML model into a production environment. Be prepared to explain your choice of architecture, how you handled data pipelines, and the specific metrics you used to evaluate success beyond just model accuracy, such as latency, reliability, and cost efficiency.
Demonstrate expertise in the GenAI and VectorDB stack
Review your experience with Generative AI frameworks, prompt engineering, and LLM fine-tuning. Prepare to discuss how you have integrated VectorDBs into production applications to support retrieval-augmented generation or similar workflows, emphasizing security and guardrails.
Articulate your approach to DevSecOps and TDD
Prepare to discuss how you incorporate security standards and test-driven development into your AI workflows. Be ready to explain your experience with pipeline automation on AWS, specifically using AWS Sagemaker to maintain robust, scalable, and reliable AI components.
Develop a narrative for cross-functional communication
Prepare examples of how you have communicated complex analytical findings to non-technical stakeholders, such as operational leaders or product owners. Focus on how you translated technical trade-offs into business outcomes and operational impact.
Formulate strategic questions for the interviewers
Prepare thoughtful questions regarding how the team balances innovation with the high-reliability requirements of airline operations. Ask about the current challenges in democratizing AI within Delta and how the team approaches the lifecycle management of models in a distributed environment.

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