New opportunity

Technical Lead - AI

E2M Solutions · Ahmedabad, India

About this role

E2M Solutions is seeking a hands-on Technical Lead – AI in Ahmedabad to design AI architectures, lead engineers, and deliver production-ready AI/ML solutions and intelligent automation systems for business needs.

Responsibilities

Lead the design and development of AI solutions; mentor AI engineers and developers; conduct architecture and code reviews and technical evaluations; and promote coding and application-development best practices.
Build chatbots, recommendation engines, NLP systems, predictive analytics models, LLM-based applications, scalable AI APIs, and automated AI workflows. Integrate AI into applications and collaborate with cross-functional teams to turn business requirements into working solutions.
Evaluate emerging AI, machine learning, and generative AI tools and frameworks, and contribute to internal innovation initiatives.

Requirements

5–8 years of overall AI/ML development experience, including at least 2 years in a Technical Lead or Senior Engineer role. Experience building production-level AI applications and leading or mentoring engineers is required.
Experience with generative AI and LLMs; LangChain, LlamaIndex, or similar orchestration frameworks; NLP; AI APIs and application integration; and familiarity with vector databases, embeddings, and RAG architectures. Working knowledge of full-stack development is required.

Preferred

A background in service-based or product engineering companies.

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

Skills for this role

Artificial IntelligenceMachine LearningGenerative AILarge Language ModelsLangChainLlamaIndexNatural Language ProcessingVector DatabasesEmbeddingsRetrieval-Augmented GenerationPrompt EngineeringAI APIsAI Workflow AutomationChatbotsRecommendation SystemsPredictive AnalyticsFull Stack DevelopmentTechnical LeadershipTeam MentoringArchitecture ReviewCode Review

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 architecture and production delivery
The role leads AI/ML solution design and builds scalable AI APIs and production-ready systems. Prepare to explain architectural decisions and how an AI application moves from design into use.
Lead through hands-on technical review
The lead mentors engineers, conducts architecture and code reviews, and promotes coding and application-development best practices. Prepare examples of technical guidance that improved a team's implementation.
Turn business needs into integrated AI solutions
The role collaborates across functions and integrates AI into applications, with work spanning chatbots, recommendations, NLP, predictive analytics, and automation. Prepare to discuss how requirements shaped a solution and its integration.
Apply and evaluate LLM approaches
The posting calls for LLM applications, prompt engineering, orchestration frameworks, and familiarity with embeddings, vector databases, and RAG. It also asks the lead to evaluate emerging tools and contribute to internal AI initiatives.

Likely challenges to think through

Balancing experimentation with production readiness
Preparation hypothesis: evaluating new AI tools while delivering production-ready systems may require choosing between promising capabilities and implementation stability. Prepare a concrete example of how you assessed that trade-off; this is not a confirmed employer problem.
Maintaining technical depth while leading others
Preparation hypothesis: hands-on development, reviews, and mentoring may compete for a lead's time. Prepare to explain how you have kept design and code quality moving while enabling engineers to own work.
Choosing the right approach across varied use cases
Preparation hypothesis: the range from predictive models to LLM workflows makes it important not to default to one technique. Prepare to explain how business requirements would guide the choice of approach and application integration.

Practice your answers

Technical practice

How would you turn a business need into an AI solution architecture?

What to coverThe role involves designing AI architectures and translating business needs into working solutions. Suggested preparation: explain how you would clarify the objective, compare AI and non-AI approaches, define components and interfaces, and check that the design can be delivered.

How would you design a retrieval-augmented generation application?

What to coverThe posting names embeddings, vector databases, and RAG architectures. Suggested preparation: explain document preparation, retrieval, grounding, and how you would evaluate answer relevance against retrieval cost and latency.

How would you improve an LLM application's output through prompt engineering?

What to coverThe role includes LLM-based applications and prompt engineering. Suggested preparation: describe how you would test prompts against representative cases, identify failure patterns, and decide when a prompt change is insufficient.

When would you use an AI orchestration framework?

What to coverThe posting asks for experience with LangChain, LlamaIndex, or similar frameworks and includes AI workflow automation. Suggested preparation: compare a framework with simpler application code for managing workflow steps, considering maintainability and control.

How would you design and evaluate an NLP-powered chatbot?

What to coverChatbots and NLP systems are listed among the solutions this lead may develop. Suggested preparation: cover the intended user task, language-handling challenges, response quality, and how you would handle requests the system cannot answer reliably.

How would you approach a recommendation-engine problem?

What to coverRecommendation engines are explicitly listed as an example of AI-powered solutions. Suggested preparation: discuss the available signals, a suitable baseline, evaluation measures, and trade-offs such as relevance versus coverage.

How would you validate a predictive analytics model before use?

What to coverPredictive analytics models are another stated solution area. Suggested preparation: explain target definition, data quality, a meaningful baseline, and validation that reflects the intended business use rather than only a model score.

How would you expose an AI capability through a scalable API?

What to coverThe role calls for scalable AI APIs and integration into applications, with working knowledge of full-stack development. Suggested preparation: explain the API contract, application integration, and trade-offs around response time, failures, and changing demand.

What would you check before calling an AI system production-ready?

What to coverThe posting calls for production-ready AI systems and experience building production-level AI applications. Suggested preparation: describe checks for solution quality, reliability, and maintainability, and how you would detect and investigate failures after release.

How would you evaluate a new AI tool or framework during an architecture review?

What to coverThe lead conducts architecture reviews and technical evaluations and assesses emerging AI technologies. Suggested preparation: compare a candidate tool against the project's needs, integration effort, limitations, and evidence from a small technical evaluation.

Behavioural practice

Tell me about a time you mentored an engineer through a difficult AI or development problem.

What to coverMentoring AI engineers and developers is a stated responsibility. Suggested preparation: use situation, action, and result to show how you guided the engineer while preserving their ownership of the work.

Tell me about a time you translated an unclear business need into a technical plan.

What to coverThe role requires collaboration with cross-functional teams to turn business needs into AI solutions. Suggested preparation: describe the situation, how you clarified priorities and trade-offs, and the resulting decision or deliverable.

Tell me about a time a code or architecture review led to a difficult technical decision.

What to coverThe lead is expected to conduct code and architecture reviews and promote development best practices. Suggested preparation: give the situation, the evidence and actions you used to resolve differing views, and the outcome.

Tell me about a time you resolved a significant problem while delivering a production AI application.

What to coverThe posting emphasizes hands-on leadership, complex AI problem-solving, and production-level applications. Suggested preparation: explain the situation, your diagnostic and coordination actions, and the result without implying a particular type of incident is required.

Tell me about a time you assessed a new AI approach for a team or initiative.

What to coverEvaluating new technologies and contributing to internal AI initiatives are stated duties. Suggested preparation: describe the situation, how you tested the approach and communicated its trade-offs, and what decision followed.

Sharpen your knowledge

Topics to prepare

Production AI architecture
Posting: The lead designs AI/ML architectures and delivers production-ready systems. Suggested revision exercise: sketch an architecture for an AI feature, then explain its scaling, failure-handling and review decisions.
LLMs, prompting and orchestration
Posting: The role covers LLM applications, prompt engineering and LangChain, LlamaIndex or similar frameworks. Suggested revision exercise: explain how a prompt and an orchestration workflow would turn a business request into a response, including where you would evaluate output quality.
Embeddings and RAG
Posting: Familiarity with vector databases, embeddings and RAG architectures is required. Suggested revision exercise: diagram document preparation, retrieval and response generation, and explain how you would investigate irrelevant retrieved results.
NLP and other AI use cases
Posting: Strong NLP knowledge is required; chatbots, recommendation engines and predictive analytics models are named solution types. Suggested revision exercise: choose one use case and explain its inputs, expected outputs and how you would assess whether it serves the business need.
Technical leadership and evaluation
Posting: The lead mentors engineers, conducts architecture and code reviews, and evaluates AI technologies. Suggested revision exercise: prepare a review discussion that identifies a design trade-off, gives actionable feedback and explains how you would compare two approaches.

Skills to revise

Build a small LLM workflow
Suggested hands-on exercise: implement a chatbot flow with prompt handling and an orchestration framework named in the posting, then test several inputs and revise weak responses. This practices the posting's LLM application and workflow responsibilities.
Prototype retrieval-augmented responses
Suggested hands-on exercise: use a small set of documents to generate embeddings, retrieve relevant passages and produce grounded answers; inspect a failed retrieval. This practices the stated RAG, vector-database and embedding familiarity.
Implement and integrate an AI API
Suggested hands-on exercise: expose one AI feature through an API and connect it to a simple application interface; check input handling and unsuccessful responses. This reflects the required AI API, application-integration and full-stack knowledge.
Practice a technical review
Suggested hands-on exercise: review a sample AI solution design and code change, write prioritized feedback, and explain it as you would to an engineer. This reflects the role's architecture reviews, code reviews and mentorship.

Your preparation checklist

Map the stated requirements to evidence
The posting asks for 5–8 years in AI/ML, including 2+ years as a Technical Lead or Senior Engineer, plus production AI applications and team leadership. Prepare a brief timeline with specific examples relevant to each requirement.
Build a production AI project walkthrough
The role calls for production-ready AI systems and scalable AI APIs. Prepare one architecture diagram and a walkthrough of the business need, implementation, application integration, deployment decisions, and results you can substantiate.
Practice an LLM architecture exercise
The posting names LLMs, prompt engineering, orchestration frameworks, embeddings, vector databases, and RAG. As a preparation exercise, sketch a chatbot architecture and explain retrieval, API boundaries, evaluation, and trade-offs.
Prepare a leadership and review example
The role includes mentoring engineers and conducting architecture and code reviews. Prepare one example showing the feedback you gave, a technical decision you helped resolve, and the outcome.
Select examples across the stated solution types
The posting mentions NLP systems, recommendation engines, predictive models, and AI workflow automation. Identify relevant work you can discuss; for each example, note its objective, your contribution, and how you measured its performance.
Plan a tool-evaluation discussion
The role includes evaluating emerging AI tools and frameworks. Prepare a short comparison of tools you have used, such as LangChain, LlamaIndex, or similar frameworks, with criteria and limitations rather than a preferred-tool claim.
Write questions about delivery expectations
E2M describes work with digital agencies and cross-functional teams. Prepare questions about the first AI use cases, how business requirements reach the engineering team, and expectations for production support and mentoring.

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

KEEP EXPLORING

Similar AI jobs

Related skills and specializations in India. Matched to this role, not your profile.

Explore more jobs

Finding similar opportunities…