New opportunity

Senior AI Engineer

Eastman · Hyderabad, India

About this role

Eastman’s Generative AI team is developing enterprise solutions for Commercial, Technology, and Operations functions. The Senior AI Engineer will lead the design, development, and adoption of AI technology and analytics, contribute to AI strategy, and work with cross-functional teams to roll out solutions. The role includes leading projects that deploy AI agents to automate work.

Responsibilities

  • Design and implement applications using large language models, and lead the design and development of agentic AI solutions.
  • Prepare data, fine-tune models, and build data, analytics, and machine learning pipelines.
  • Develop AI solutions around business requirements; use Azure and/or AWS to deploy and manage models and solutions.
  • Partner with business leaders and stakeholders, present insights and solutions, and explain technical results to non-technical audiences.
  • Apply advances in AI to business challenges and help improve enterprise adoption and proficiency with AI tools.

Required qualifications

  • Bachelor’s or master’s degree in Computer Science, Mathematics, Statistics, or a related field, and at least 8 years of experience in software engineering, data science, AI, or a related field.
  • Software development and statistical analysis proficiency; experience with large language models, solution architecture and design, and Azure and/or AWS.
  • Proficiency in Python, R, or a similar language; strong communication and problem-solving skills, with the ability to work independently and in a team.

Preferred qualifications

Experience with large language models and supporting workflows, project leadership and development, Agile development, and applications in sales, marketing, or market research.

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

Skills for this role

Agentic AILarge Language ModelsModel fine-tuningData pipelinesMachine learning pipelinesAzureAWSPythonRStatistical analysisSolution architectureAgile developmentProject leadershipStakeholder communication

Your skill match

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Get interview-ready for this role

A focused preparation guide, built around this job’s responsibilities and requirements.

✦ AI-generated guide
Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.
01

Understand the role

What matters most

  • Lead LLM and agentic solution delivery

    Eastman calls for designing LLM applications and leading projects that deploy agents to automate work. Prepare to explain how you would take an agentic solution from a business requirement through design, implementation, and rollout.

  • Build the supporting data and model workflows

    The role includes preparing data, fine-tuning models, and building data, analytics, and machine-learning pipelines. Prepare to discuss how those components support an application, rather than treating the model as a standalone deliverable.

  • Architect and deploy enterprise AI solutions

    The posting requires solution architecture and design, plus hands-on use of Azure and/or AWS to deploy and manage AI models and solutions. Prepare to explain deployment and management choices for a business-facing system.

  • Connect AI work to business adoption

    The engineer partners with leaders across Commercial, Technology, and Operations, contributes to AI strategy, explains results to non-technical audiences, and helps improve adoption of AI tools. Prepare to present a technical decision in terms of the business need it serves.

Likely challenges to think through

  • Balancing agent automation with dependable operation

    Preparation hypothesis, not a stated Eastman problem: agents that automate work may need limits on autonomy to produce dependable outcomes. Prepare to explain how you would decide what an agent can do, how its outputs would be checked, and when a person should remain involved.

  • Choosing the right level of model customization

    Preparation hypothesis: because the role covers both LLM applications and fine-tuning, a challenge may be deciding when customization justifies the data and maintenance effort. Prepare to compare approaches against a specific business requirement without assuming fine-tuning is always necessary.

  • Reconciling needs across functions

    Preparation hypothesis: solutions spanning Commercial, Technology, and Operations may involve different expectations, while enterprise adoption favors approaches people can use consistently. Prepare to describe how you would clarify needs, explain trade-offs to non-technical stakeholders, and sequence a rollout.

03

Practice your answers

Technical practice

How would you turn a business requirement into an LLM solution design?

What to coverSuggested answer focus: Clarify the user's task, available data, expected outputs, and how success will be measured before choosing an architecture. Explain trade-offs between solution complexity, maintainability, and the business need.

How would you design an AI agent to automate a work process?

What to coverSuggested answer focus: Define the task boundary, the information and actions the agent needs, and where human review should remain. Describe how you would test whether the automation completes the intended work reliably.

What would you consider when building an application around an LLM?

What to coverSuggested answer focus: Explain how the application supplies relevant context, handles outputs, and fits into the surrounding software system. Discuss trade-offs in response quality, latency, and cost as design considerations rather than assumed Eastman requirements.

How would you prepare data for an AI solution?

What to coverSuggested answer focus: Describe how you would assess relevance, quality, and suitability for the intended business task. Explain how data-preparation choices affect downstream model behavior and evaluation.

When would you fine-tune a model, and how would you assess the result?

What to coverSuggested answer focus: Compare fine-tuning with using an existing model without further training, based on the task and available data. Describe a baseline and evaluation approach that could show whether fine-tuning adds value.

How would you structure data, analytics, and machine-learning pipelines for an AI application?

What to coverSuggested answer focus: Trace the flow from source data through preparation, model-related steps, and usable outputs. Explain how you would make failures identifiable and changes testable as the pipeline evolves.

How would you deploy and manage an AI solution on Azure or AWS?

What to coverSuggested answer focus: Describe the deployment components and operational decisions you would need to make on a cloud platform you know. Discuss how you would assess performance, cost, and ongoing management without assuming a particular Eastman cloud setup.

How would you evaluate whether an AI solution meets a business need?

What to coverSuggested answer focus: Connect technical measures to the intended business outcome, and explain how you would use statistical analysis where appropriate. Address the limits of an evaluation and what evidence would justify a rollout decision.

How would you diagnose an LLM or agent solution that produces unreliable results?

What to coverSuggested answer focus: Break the problem into possible data, model, application, and workflow causes. Describe how you would isolate a cause, test a change, and check that the change improves the intended task.

Behavioural practice

Tell me about a time you aligned a technical solution with business stakeholders' needs.

What to coverSuggested answer focus: Use a real situation, your actions to clarify requirements and resolve differing priorities, and the result. Make clear how stakeholder input changed the solution or its scope.

Describe a project you led from AI solution design toward rollout.

What to coverSuggested answer focus: Set out the situation, the decisions and coordination you personally led, and the result. Explain how you handled a delivery obstacle without implying any particular Eastman project process.

Give an example of explaining a complex technical result to a non-technical audience.

What to coverSuggested answer focus: Describe the audience and decision at stake, how you adapted your explanation, and the result. Note how you communicated uncertainty or limitations if they mattered.

Tell me about working across teams to support adoption of a new tool or solution.

What to coverSuggested answer focus: Describe the situation, the actions you took to understand concerns or build proficiency, and the result. Distinguish delivering the technology from helping people use it effectively.

Describe a time you balanced independent problem-solving with team collaboration.

What to coverSuggested answer focus: Use a real situation to show what you handled independently, when you sought input, and the result. Explain how that balance helped resolve the problem or move the work forward.

02

Sharpen your knowledge

Topics to prepare

  • LLM and agentic solution design

    Posting: The role leads the design of LLM applications and agentic AI solutions, including agents that automate work. Suggested revision exercise: diagram an agent workflow for a hypothetical business task, showing what the model does, what the application controls, and where a person reviews the result.

  • Data preparation, fine-tuning, and pipelines

    Posting: Responsibilities include preparing data, fine-tuning models, and building data, analytics, and ML pipelines. Suggested revision exercise: trace a sample dataset from preparation through model adaptation and evaluation, noting how you would check data quality and results.

  • Solution architecture and cloud deployment

    Posting: Solution design experience and hands-on Azure and/or AWS experience are required for deploying and managing AI solutions. Suggested revision exercise: sketch an architecture for an LLM application on one of those platforms and explain its deployment and management decisions.

  • Statistical analysis for business problems

    Posting: Statistical analysis proficiency is required, and solutions must address business requirements. Suggested revision exercise: define a measurable outcome for a hypothetical Commercial, Technology, or Operations use case and explain how you would assess whether an AI solution improves it.

  • Cross-functional delivery and adoption

    Posting: The engineer partners with leaders, leads projects, explains results to non-technical audiences, and supports enterprise AI adoption. Suggested revision exercise: outline how you would turn a stakeholder request into a project plan and present its outcome in plain language.

Skills to revise

  • Prototype an LLM workflow

    Posting: The role designs and implements LLM systems and leads agentic AI development; Python, R, or a similar language is required. Suggested practice: use a language you know to prototype a small, hypothetical agent workflow and demonstrate how it handles an unsuccessful step.

  • Build a reproducible data pipeline

    Posting: Data preparation and data, analytics, and ML pipelines are explicit responsibilities. Suggested practice: write a small pipeline that validates input records, transforms them, and produces a repeatable output; explain how it could support model work.

  • Plan a cloud release

    Posting: Hands-on Azure and/or AWS experience is required for deploying and managing AI models and solutions. Suggested practice: on a platform you know, walk through how you would deploy a small AI application, check that it works, and manage an update.

  • Analyze and present results

    Posting: Statistical analysis and communicating technical results to business stakeholders are required. Suggested practice: analyze a small example dataset, state the limits of the conclusion, then give a two-minute explanation for a non-technical audience.

  • Lead a stakeholder design discussion

    Posting: The role involves business-partner collaboration and project leadership; Agile familiarity is preferred. Suggested practice: rehearse a discussion that clarifies the business task, agrees on a measurable outcome, and identifies decisions needed before an agent solution is built.

04

Your preparation checklist

  • Map your evidence to the qualifications

    Create a one-page matrix of examples for the posting’s 8+ years of relevant experience, solution design, software development, statistical analysis, Python/R or similar, LLM applications, and Azure and/or AWS. Mark any gaps rather than inventing examples.

  • Prepare an agentic AI project walkthrough

    Select a project you can discuss, if available, and explain the business need, LLM and agent design, your leadership decisions, deployment, and results. The role specifically involves leading projects that deploy agents to automate work.

  • Practice an enterprise solution design

    Sketch an AI solution for a hypothetical Commercial, Technology, or Operations need. Be ready to explain architecture choices, how requirements shape the design, and how you would deploy and manage it on Azure and/or AWS; present it as an exercise, not an Eastman project.

  • Explain the data and model workflow

    Prepare a diagram showing data preparation, possible fine-tuning decisions, and data, analytics, and ML pipelines. Identify where statistical analysis would inform your choices; these activities appear in the posting.

  • Rehearse a business-stakeholder briefing

    Turn one technical example into a short explanation of the business need, proposed solution, results, and limitations for a non-technical audience. The role calls for partnering with leaders and communicating technical results.

  • Document leadership and adoption examples

    Prepare concise accounts of cross-functional project leadership, independent problem-solving, and helping others use AI tools, where you have evidence. Include Agile or sales, marketing, or market-research work only if relevant to your experience; the posting lists these as preferred.

  • Bring targeted questions for Eastman

    Write questions about which Commercial, Technology, or Operations needs are priorities, how the team evaluates agent deployments, and what successful rollout and enterprise AI adoption would look like in this role.

AI-generated from the job information · Prepared Sep 30, 2026 (UTC)Useful preparation, not a guarantee of eligibility or an offer.

Availability last checked: 5 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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