New opportunity

Sr SW engineer- AI/ML

GE Vernova · Hyderabad; Bangalore, India

About this role

Lead enterprise full-stack and AI platform architecture across multiple initiatives, helping globally distributed teams integrate AI into Grid software services. Design and deliver production AI systems while balancing reliability, performance and cloud cost, and establish standards for AI-assisted development.

Responsibilities

Deliver reusable AI components for forward-deployed engineers; ship resilient REST APIs and microservices.
Build RAG and multimodal LLM features and design agentic workflows for autonomous tasks.
Make end-to-end technical decisions, mentor engineers and guide contractors and partners.

Required qualifications

Bachelor’s degree in computer science, computer engineering or a related technical field.
10+ years of experience with at least one general-purpose programming language—Python preferred, or Java or Go—and front-end experience with JavaScript or TypeScript.
Proven record of shipping production applications incorporating LLMs or AI services at scale; experience driving at least 5 projects with substantial AI and ML engagement.
Experience leading technical teams and guiding external contractors or vendors; strong cloud platform experience with AWS, GCP or Azure and containerization with Docker and Kubernetes.
Experience with technical oversight, code reviews and quality assurance, plus strong troubleshooting and communication skills and prior work with globally distributed teams.

Preferred qualifications

AI/ML architecture, technical leadership and system optimization experience; a master’s degree or equivalent in AI is a plus.
Experience with SOAP, REST or MuleSoft integration, advanced state management, vector databases such as Milvus, Pinecone, Weaviate or pgvector, and embedding model optimization.
Experience with multiple LLM providers, orchestration frameworks such as LangChain or LlamaIndex, enterprise RAG, semantic search optimization and conversation memory at scale; experience scaling AI-powered features for 100k+ users.

The full-time position lists Hyderabad and Bangalore locations in India. Relocation assistance is provided.

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

Skills for this role

AI platform architectureMicroservicesREST APIsRAGMultimodal LLMsAgentic workflowsPythonJavaGoJavaScriptTypeScriptAWSGCPAzureDockerKubernetesTechnical leadershipCode reviewQuality assuranceTroubleshootingSOAPMuleSoftState managementMilvusPineconeWeaviatepgvectorEmbedding model optimizationLangChainLlamaIndex

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

Own AI platform architecture
The role leads enterprise full-stack and AI platform architecture across multiple initiatives, making end-to-end decisions that balance reliability, performance and cloud cost.
Deliver reusable production services
The posting calls for reusable AI components for forward-deployed engineers, resilient REST APIs and microservices, and a record of shipping AI applications at scale.
Build advanced AI features
Core work includes RAG and multimodal LLM features and agentic workflows for autonomous tasks. Enterprise retrieval, semantic search and conversation memory are preferred areas of experience.
Lead implementation across teams
The engineer mentors engineers, guides contractors and partners, and works with globally distributed teams to integrate AI into Grid software services. Technical oversight, code review and quality assurance are also required.

Likely challenges to think through

Balancing production trade-offs
A likely difficulty is improving AI-system reliability and performance without disproportionate cloud cost. Prepare a concrete architecture example showing the trade-offs measured, the decision made and how production behavior was monitored.
Making reuse work across services
Reusable components may need to serve different Grid software integrations without becoming too rigid or too bespoke. Prepare to explain where you would standardize APIs and components versus allow service-specific adaptation; this is a preparation hypothesis, not a reported problem.
Controlling autonomous feature behavior
RAG, multimodal features and autonomous workflows may be harder to test than conventional services because outputs and tool interactions can vary. Prepare to discuss evaluation, troubleshooting and limits on autonomous actions as proposed design approaches, not stated employer requirements.

Practice your answers

Technical practice

How would you architect an AI platform serving multiple Grid software initiatives?

What to coverThe posting calls for enterprise full-stack and AI platform architecture across multiple initiatives. Suggested preparation: explain how you would define shared services and boundaries while balancing reliability, performance and cloud cost.

How would you design reusable AI components for forward-deployed engineers?

What to coverDelivering reusable AI components is a stated responsibility. Suggested preparation: discuss component interfaces, configuration, documentation and versioning, including where reuse could constrain a team's needs.

How would you make a production AI REST API resilient?

What to coverThe role involves shipping resilient REST APIs and microservices; SOAP and MuleSoft integration are preferred experience. Suggested preparation: address failure handling, timeouts, compatibility and observability, and explain how you would choose an integration approach.

How would you improve an enterprise RAG system with poor answer relevance?

What to coverThe posting names RAG, semantic search, vector databases and embedding optimization. Suggested preparation: distinguish retrieval failures from generation failures, then describe how you would evaluate changes to embeddings, indexing, ranking and answer quality.

What would you consider when building a multimodal LLM feature?

What to coverBuilding multimodal LLM features is a stated responsibility. Suggested preparation: explain how you would handle different input types, evaluate output quality and account for latency, cost and failures in production.

How would you design an agentic workflow for an autonomous task?

What to coverThe posting explicitly calls for agentic workflows for autonomous tasks. Suggested preparation: describe task boundaries, tool access, state, stopping conditions and safeguards, including when a simpler workflow would be preferable.

How would you manage state across an AI-powered front end and service?

What to coverJavaScript or TypeScript front-end experience is required, while advanced state management and conversation memory at scale are preferred. Suggested preparation: distinguish UI state from persisted conversation context and discuss consistency, privacy and growth over time.

How would you compare LLM providers and orchestration approaches?

What to coverExperience with multiple LLM providers and frameworks such as LangChain or LlamaIndex is preferred. Suggested preparation: compare quality, latency, cost and operational complexity, and explain when a framework helps versus when direct integration is simpler.

How would you deploy and scale an AI service in the cloud?

What to coverThe posting requires cloud-platform experience and Docker and Kubernetes, and prefers experience scaling AI-powered features for 100k+ users. Suggested preparation: discuss capacity, deployment and monitoring choices, including how you would test performance and control cost as usage grows.

How would you investigate a production AI failure and prevent its recurrence?

What to coverTroubleshooting, code reviews, quality assurance and standards for AI-assisted development all appear in the posting. Suggested preparation: walk through isolating a service, retrieval or model issue, then explain what tests, review checks or standards you would change based on the evidence.

Behavioural practice

Tell me about an end-to-end technical decision you led across teams.

What to coverThe role calls for end-to-end decisions and leadership across multiple AI initiatives. Suggested preparation: use a real situation, action and result to show how you weighed competing technical priorities and communicated the decision.

Describe a time you mentored engineers or guided an external partner.

What to coverMentoring engineers and guiding contractors or partners are stated responsibilities. Suggested preparation: give a real situation, the guidance and oversight you provided, and the resulting change in delivery or quality.

Tell me about coordinating technical work with a globally distributed team.

What to coverThe posting calls for communication and prior work with globally distributed teams. Suggested preparation: use a real situation, action and result to explain how you resolved ambiguity or maintained alignment across locations.

Describe a production issue you helped resolve under pressure.

What to coverStrong troubleshooting and production AI delivery are stated requirements. Suggested preparation: describe the actual failure, your diagnostic actions and the result, separating what was known at the time from what you learned afterward.

Tell me about driving several AI or ML projects with competing priorities.

What to coverThe posting seeks experience driving multiple projects with substantial AI and ML engagement. Suggested preparation: choose a real situation and explain your prioritization actions, trade-offs and outcomes without implying every project followed the same approach.

Sharpen your knowledge

Topics to prepare

Enterprise AI platform architecture
Sketch how reusable AI components could serve several Grid software initiatives, then explain where you would draw service boundaries and balance reliability, performance and cloud cost. The posting explicitly calls for architecture across multiple initiatives and end-to-end technical decisions.
Production LLM system design
Walk through an AI feature from request to response, identifying how you would assess behavior at scale and troubleshoot failures. Shipping production applications incorporating LLMs or AI services at scale is a required qualification.
RAG and semantic search
Diagram a retrieval-to-answer flow and compare two ways to improve retrieval quality. RAG features are an explicit responsibility; vector databases, embedding optimization, enterprise RAG and semantic search optimization are preferred experience.
Multimodal and agentic workflows
Outline a multimodal feature and an autonomous-task workflow, including the decisions each component makes. Building multimodal LLM features and designing agentic workflows are stated responsibilities.
Technical leadership across distributed teams
Prepare a concise account of how you would communicate an architectural decision, review its implementation and coordinate engineers and external partners across locations. Leadership, technical oversight, mentoring and distributed-team communication are explicit in the posting.

Skills to revise

Build a resilient AI-backed API
In Python, or Java or Go, implement a small REST endpoint that calls a stubbed AI service; test failure responses and explain your service boundaries. The role calls for a general-purpose language and delivery of resilient REST APIs and microservices.
Containerize and deploy a service
Package that endpoint with Docker and draft a Kubernetes deployment for one of AWS, GCP or Azure; rehearse how you would investigate a failed rollout. Cloud platforms, Docker, Kubernetes and troubleshooting are required experience.
Implement a small retrieval flow
As a preparation exercise, index a small document set, retrieve passages for a question and inspect a poor match. If using a vector database, choose one named in the preferred qualifications, such as pgvector or Milvus; it is not a required tool.
Connect a front end to an AI service
Use JavaScript or TypeScript to build a small interface that calls your REST endpoint and displays its response and errors. Front-end experience in one of those languages is required, alongside the role’s full-stack scope.
Practice code review and quality assurance
Review a sample AI-service change for correctness, failure behavior and test coverage; write actionable comments and a short troubleshooting plan. Code review processes, quality assurance and troubleshooting are required experience.

Your preparation checklist

Build a requirements evidence sheet
The posting requires a related bachelor’s degree, 10+ years with a general-purpose language, JavaScript or TypeScript front-end experience, and cloud and containerization experience. Prepare a one-page sheet of verifiable examples for each; mark any gaps rather than inventing evidence.
Map your AI/ML project record
The posting asks for experience driving at least five projects with substantial AI/ML engagement. If applicable, prepare a brief record for each showing your role, decisions, production outcome, and scale.
Rehearse one production AI walkthrough
Choose a shipped LLM or AI-service application you can discuss. Diagram its architecture and explain your end-to-end decisions, reliability measures, performance and cloud-cost trade-offs, and what you would change.
Practice an enterprise AI design exercise
Sketch a reusable component for Grid software services with a resilient REST API or microservice. As a preparation exercise, explain how you would evaluate RAG, multimodal features, or agentic workflows where appropriate; those features are named in the role, not specified as one required design.
Prepare an operations and integration example
Have a concrete account of troubleshooting a production issue, including diagnosis, testing, code review, and resolution. Be ready to discuss relevant Docker, Kubernetes, cloud, and integration choices; vector databases and orchestration frameworks are listed as preferred experience.
Document a technical leadership example
Prepare a concise example of a decision you led across engineers or globally distributed teams, including how you mentored others, handled review and quality standards, and guided contractors or partners if applicable.
Write questions for the employer
Ask which Grid software services need AI integration first, what reusable components forward-deployed engineers need, and how the team evaluates reliability, performance, and cloud cost. These are questions to clarify the role, not assumed company practices.

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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