New opportunity

Principal Scientist-AI Research

Epergne Solutions · Bengaluru, India

About this role

The Principal Scientist-AI Research will lead research and development in generative AI, large language models (LLMs), multimodal AI and agentic systems, translating research outcomes into scalable, enterprise-grade AI solutions. The stated job location is Bengaluru, India.

Responsibilities

Define the research roadmap across LLMs, multimodal AI, generative AI, NLP and agentic systems. Lead experiments, benchmarking and publication of research outputs.
Design and optimize algorithms for large-scale model training, inference, reasoning, memory and autonomous systems. Develop architectures using retrieval-augmented generation (RAG), prompt orchestration and agentic workflows.
Build and optimize distributed training pipelines, inference systems and cloud-based AI platforms. Work with engineering and product teams to deploy solutions in production.
Mentor scientists and engineers, establish research best practices, contribute to patents and reusable research assets, and promote responsible AI and reproducibility.

Required qualifications and skills:

A master’s degree or PhD in Computer Science, Artificial Intelligence, Machine Learning or a related field, with 10–15 years of experience in AI research, machine learning or advanced AI technology development.
Demonstrated leadership of AI research initiatives and delivery of production-ready AI solutions. Deep expertise in LLMs, generative AI, multimodal AI, NLP and agentic AI; strong knowledge of machine learning, deep learning, distributed systems and model optimization.
Experience with RAG, prompt engineering, scalable AI deployment, cloud platforms (AWS, Azure or GCP), MLOps/LLMOps and real-time inference. Proficiency in Python and frameworks such as PyTorch or TensorFlow. Strong research, analytical, problem-solving, mentoring and cross-functional collaboration skills.

Preferred qualifications and attributes:

A strong publication record and contributions to AI research, patents or open-source projects. Scientific leadership, an innovation mindset, experience commercializing research or working with enterprise AI platforms, and the ability to turn frontier research into practical business and product outcomes. Excellent communication and stakeholder engagement skills.

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

Skills for this role

AI researchGenerative AILarge Language ModelsMultimodal AINatural Language ProcessingAI agentsMachine learningDeep learningDistributed systemsModel optimizationRetrieval-augmented generationPrompt engineeringPythonPyTorchTensorFlowAWSAzureGoogle Cloud PlatformMLOpsLLMOpsReal-time inferenceDistributed trainingBenchmarkingResponsible AIResearch leadershipMentoringStakeholder engagement

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

Set the research direction
Define and drive the roadmap across LLMs, multimodal AI, generative AI, NLP and agentic systems; lead experiments, benchmarking and publication of research outputs.
Advance models and architectures
Design and optimize algorithms for training, inference, reasoning, memory and autonomous systems, alongside scalable RAG, prompt-orchestration and agentic architectures.
Turn research into production systems
Build distributed training pipelines, inference systems and cloud-based AI platforms, working with engineering and product teams to deploy enterprise-grade solutions.
Lead scientists and strengthen research practice
Mentor scientists and engineers, establish research best practices, and contribute to reusable research assets and patents.
Maintain responsible, reproducible research
Promote responsible AI and reproducibility while leading research initiatives and evaluating their results.

Likely challenges to think through

Choosing where to concentrate research
Preparation hypothesis: A roadmap spanning several AI fields may require trading breadth against depth. Prepare to explain how you would select research bets and use experiments and benchmarks to revisit them.
Balancing novelty with deployability
Preparation hypothesis: Promising algorithms may be difficult to run at enterprise scale or under real-time inference demands. Prepare a framework for comparing research gains with training, inference and deployment constraints.
Evaluating complex AI workflows reliably
Preparation hypothesis: RAG and agentic workflows may make results harder to reproduce and assess responsibly. Prepare to discuss evaluation design, reproducible experiments and responsible-AI checks without assuming a particular company policy.

Practice your answers

Technical practice

How would you design and benchmark an LLM research experiment?

What to coverThe posting calls for experimentation, benchmarking and high-quality research outputs. Suggested preparation (inference): explain baselines, evaluation criteria, reproducibility controls and how you would decide whether an improvement merits further development.

How would you optimize an LLM for large-scale training?

What to coverThe role includes algorithm design for large-scale model training and model optimization. Suggested preparation (inference): discuss the trade-offs among model quality, compute use and training stability, and how you would measure gains.

How would you design and evaluate a multimodal AI system?

What to coverMultimodal AI and NLP are named research areas. Suggested preparation (inference): explain how you would combine modalities, choose meaningful baselines and test whether each modality improves the intended outcome.

How would you design a scalable retrieval-augmented generation system?

What to coverThe posting explicitly calls for RAG architectures and scalable GenAI solutions. Suggested preparation (inference): address retrieval quality, grounding, answer evaluation and trade-offs between freshness, latency and cost.

How would you structure reasoning, memory and prompt orchestration in an agentic workflow?

What to coverThe role covers reasoning, memory, autonomous systems, prompt orchestration and agentic workflows. Suggested preparation (inference): describe when an agent should use stored context or take another step, and how you would evaluate reliability against a simpler design.

How would you troubleshoot a distributed training pipeline that is underperforming?

What to coverBuilding and optimizing distributed training pipelines is a stated responsibility. Suggested preparation (inference): explain how you would distinguish data, computation and communication bottlenecks, then validate that a change improves throughput without compromising results.

How would you optimize a real-time inference system on a cloud platform?

What to coverThe posting names inference systems, real-time inference and experience with AWS, Azure or GCP. Suggested preparation (inference): discuss latency, throughput, model quality and cost trade-offs, including how you would test performance under load.

How would you move a research model into a production-ready AI solution?

What to coverThe role requires collaboration with engineering and product teams, scalable deployment and MLOps/LLMOps experience. Suggested preparation (inference): outline validation, versioning, monitoring and rollback considerations, and identify decisions that need cross-functional agreement.

How would you make AI research reproducible and responsible?

What to coverPromoting responsible AI and reproducibility is an explicit responsibility. Suggested preparation (inference): discuss documenting data and experiments, checking relevant failure modes, and balancing research progress with deployment risks.

How would you decide whether a new algorithm is worth turning into a reusable research asset?

What to coverThe posting calls for novel algorithms, research best practices and reusable research assets. Suggested preparation (inference): explain how you would assess evidence of improvement, generality, maintenance effort and potential production value.

Behavioural practice

Tell me about a time you set a research direction amid competing priorities.

What to coverThe role calls for defining and driving a roadmap across several AI research areas. Suggested preparation (inference): use a real situation, describe how you chose and communicated priorities, and state the result without implying every avenue succeeded.

Describe a time you helped move research into a product or production environment.

What to coverThe posting emphasizes collaboration with engineering and product teams to deliver practical AI solutions. Suggested preparation (inference): give the situation, your actions to resolve differing requirements, and the resulting decision or outcome.

Tell me about a time you mentored scientists or engineers.

What to coverMentoring and establishing research best practices are stated responsibilities. Suggested preparation (inference): identify the situation, what you did to develop others or improve a practice, and the observable result.

Describe a time you communicated a research result to stakeholders.

What to coverThe role includes publication of research outputs and calls for strong communication and stakeholder engagement. Suggested preparation (inference): explain the audience and situation, how you conveyed the evidence and its limitations, and what resulted.

Tell me about a time you had to balance research novelty with practical value.

What to coverThe posting seeks translation of frontier research into enterprise-grade solutions and lists research commercialisation as preferred experience. Suggested preparation (inference): describe the situation, the trade-off you made and the outcome, using an actual example rather than assuming commercialisation experience.

Sharpen your knowledge

Topics to prepare

LLMs, generative AI, NLP and multimodal models
The posting calls for deep expertise across these areas. As revision, compare two model approaches for a text-and-image task, explaining their training objectives, evaluation choices and limitations.
Agentic reasoning, memory and autonomy
The role includes designing algorithms for reasoning, memory and autonomous AI systems. As revision, sketch an agent architecture and explain when it should retain information, take an action or stop.
RAG and prompt orchestration
RAG architectures and prompt orchestration are explicit responsibilities. As revision, trace a query through retrieval, context selection and response generation; identify where answer quality can fail.
Distributed training and model optimisation
The posting specifies large-scale training, distributed systems and inference optimisation. As revision, compare training and serving bottlenecks, then explain the trade-offs in one proposed optimisation for each.
Research direction and evaluation
The role drives a research roadmap, experiments, benchmarking and publications. As revision, outline a research question with a baseline, comparison criteria and a decision rule for advancing it toward production.
Responsible and reproducible research
The posting explicitly asks for responsible AI practices and reproducibility. As revision, identify potential risks in an agentic or multimodal experiment and specify what records and checks would let a team repeat and scrutinise its results.

Skills to revise

Python model prototyping
Python and PyTorch or TensorFlow are named skills. As practice, implement a small model experiment in Python using one of those frameworks and explain how you would verify and reproduce its results.
Benchmarking an AI system
Leading experiments and benchmarking is a stated responsibility. As practice, draft a comparison table for a proposed LLM system: baseline, quality measures, inference performance, failure cases and interpretation of results.
Designing a RAG and agentic workflow
The role calls for RAG, prompt engineering and agentic workflows. As practice, draw a request-to-response flow, write example prompts and describe how you would test retrieval errors and unintended agent actions.
Planning production inference
The posting requires scalable deployment, cloud platforms, MLOps/LLMOps and real-time inference. As practice, sketch deployment and monitoring for an inference service on one named cloud option—AWS, Azure or GCP—and explain how you would investigate latency or quality regressions.
Communicating and mentoring across teams
Mentoring and collaboration with engineering and product are explicit responsibilities. As practice, prepare a short research-to-production briefing that explains an experiment's evidence, limitations and engineering decisions, then outline how you would review the work with scientists and engineers.

Your preparation checklist

Map evidence to the stated requirements
Prepare a one-page reference sheet covering the listed degree, 10–15 years of relevant experience, research leadership, production delivery, and Python with PyTorch or TensorFlow. Note which examples you can substantiate; do not treat preferred publications or patents as required.
Draft a research-roadmap proposal
As interview practice, outline how you would prioritize research across LLMs, multimodal AI, NLP, generative AI and agentic systems. Include proposed experiments, benchmarks and criteria for moving an outcome toward an enterprise solution.
Prepare a research-to-production walkthrough
Select a relevant project, if available, and explain your contribution, algorithmic choices, experimental baselines, results, reproducibility measures and production handoff. Identify any publication, patent or open-source output accurately.
Rehearse an architecture design exercise
Sketch a hypothetical scalable system using RAG, prompt orchestration or agentic workflows. Be ready to explain choices around reasoning, memory, evaluation, model optimization and responsible-AI risks; this is preparation, not a stated employer exercise.
Document scaling and inference trade-offs
Prepare a technical example or design notes on distributed training, real-time inference, cloud deployment and MLOps/LLMOps. Show the metrics and trade-offs you would use when working with engineering and product teams.
Gather leadership and collaboration examples
Prepare concise accounts of mentoring scientists or engineers, setting research practices and aligning research with product needs. Specify your actions and outcomes without implying experience you cannot evidence.
Write questions for the employer
Ask how the research roadmap is prioritized, how experiments are benchmarked and reproduced, and how research moves into production. Also clarify the team’s scope and location expectations, since the job text states Bengaluru while other page location text differs.

Availability last checked: 4 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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