Intern - AI/ML

Akino Labs; Vadodara; Hybrid; INR 134,000 - 223,000; Part time

Job summary

Akino Labs is seeking an AI/ML Intern to work with our technology and product teams on practical applications of AI, ML, generative AI, LLMs, and intelligent automation. The intern will contribute to real-world AI initiatives including AI-powered products, document intelligence, conversational systems, data processing and AI agents.

The role emphasizes applied AI/ML hands-on experience over pure research, with responsibilities across AI development, Generative AI, RAG, AI agents, data and model

Qualifications

• Strong programming fundamentals with Python.
• Familiarity with AI/ML concepts and applied AI/ML exposure.
• Experience with APIs and building AI-powered features.

Responsibilities

• Assist in developing and integrating ML/AI solutions.
• Work with structured and unstructured datasets.
• Perform data preprocessing, cleaning, transformation, and analysis.
• Evaluate ML models under guidance from senior engineers.
• Experiment with different approaches and compare model performance.

Skills

Python

LLMs

APIs

Prompt engineering

Data preprocessing

Education

Bachelor's degree in CS/AI/DS

Tools

Git/GitHub

Docker

FastAPI/Flask

SQL/MongoDB

Pinecone

Job description

Position:Intern–AI/ML

Department:AI/Technology/ProductEngineering

Location:Vadodara/Hybrid

EmploymentType:Internship

Duration:3–6Months

Experience:Freshers/0–1Year

AbouttheRole

WearelookingforanAI/MLInterntoworkwithourtechnologyandproductteamsonpracticalapplicationsofArtificialIntelligence,MachineLearning,GenerativeAI,LLMs,andintelligentautomation.

Theinternwillcontributetoreal-worldAIinitiatives,includingAI-poweredproducts,documentintelligence,conversationalsystems,dataprocessing,automation,andAIagents.

Thisroleissuitedforcandidateswhohavestrongprogrammingfundamentalsandwanthands-onexposuretoappliedAI/MLratherthanpurelyacademicresearch.

KeyResponsibilities

AI/MLDevelopment

• Assistindevelopingandintegratingmachine-learningandAIsolutions.
• Workwithstructuredandunstructureddatasets.
• Performdatapreprocessing,cleaning,transformation,andanalysis.
• ImplementandevaluateMLmodelsunderguidancefromseniorengineers.
• Experimentwithdifferentapproachesandcomparemodelperformance.
• AssistinbuildingreusableAI/MLcomponentsforproductapplications.

GenerativeAI&LLMs

• WorkwithLargeLanguageModels(LLMs)andGenerativeAIapplications.
• IntegrateAPIsfromproviderssuchas:
  • OpenAI
  • GoogleGemini
  • AnthropicClaude
  • OtherrelevantAIplatforms
• DevelopAI-poweredfeaturessuchas:
  • Conversationalassistants
  • Textgeneration
  • Summarization
  • Classification
  • Informationextraction
  • Documentanalysis
  • Questionanswering
  • AI-poweredsearch
• Experimentwithpromptengineeringandstructuredoutputs.
• EvaluateLLMresponsesforaccuracy,consistency,andrelevance.

RAG&AIApplications

• AssistindevelopingRetrieval-AugmentedGeneration(RAG)systems.
• Workwith:
  • Embeddings
  • Vectorsearch
  • Semanticsearch
  • Documentchunking
  • Retrievalpipelines
• Experimentwithvectordatabasesandretrievalstrategies.
• HelpimprovetheaccuracyandreliabilityofAI-generatedresponses.

AIAgents&Automation

• AssistindevelopingAIagentsandautomatedworkflows.
• Workwithtool/functioncallingandAPIintegrations.
• BuildworkflowswhereAIcaninteractwithexternalsystemsanddata.
• Exploremulti-stepAIworkflowsandagentorchestration.
• IdentifyrepetitivebusinessprocessesthatcanbeenhancedusingAI.

Data&ModelEvaluation

• Preparedatasetsforexperimentationandmodeldevelopment.
• Analyzemodeloutputsandidentifyfailurecases.
• Createevaluationdatasetsandtestcases.
• Trackmodelperformanceusingappropriatemetrics.
• Comparedifferentprompts,models,retrievalstrategies,orapproaches.
• Documentfindingsandrecommendations.

SoftwareEngineering

• WritecleanandmaintainablePythoncode.
• DevelopAPIsandintegrationswhererequired.
• WorkwithGit/GitHub-baseddevelopmentworkflows.
• DebugAI/MLpipelinesandapplicationissues.
• Writebasictestsforimplementedfunctionality.
• CollaboratewithsoftwareengineersandproductteamstointegrateAIfeaturesintoapplications.

RequiredSkills

Programming

• GoodunderstandingofPython.
• Understandingofbasicdatastructuresandalgorithms.
• Familiaritywithobject-orientedprogramming.
• Abilitytoread,understand,anddebugexistingcode.

AI/MLFundamentals

Basicunderstandingof:

• MachineLearningconcepts
• Supervisedandunsupervisedlearning
• Classificationandregression
• Modeltrainingandevaluation
• Overfittingandunderfitting
• Featureengineering
• Basicstatisticsandprobability
• Datapreprocessing

GenerativeAI

Basicunderstandingorhands-onexposureto:

• LLMs
• Promptengineering
• GenerativeAI
• AIAPIs
• Embeddings
• RAG
• Vectordatabases

DeepexpertiseinGenerativeAIisnotrequired.Strongfundamentalsandlearningabilityaremoreimportant.

GoodtoHave

• Pythonlibrariessuchas:
  • NumPy
  • Pandas
  • Scikit-learn
  • Matplotlib
• Familiaritywith:
  • PyTorch
  • TensorFlow
  • HuggingFace
  • LangChain
  • LlamaIndex
• Experiencewithvectordatabasessuchas:
  • Pinecone
  • Qdrant
  • Weaviate
  • Chroma
• RESTAPIsandJSON.
• FastAPIorFlask.
• SQL/MongoDB.
• GitandGitHub.
• Docker.
• Basiccloudknowledge.
• ExperiencedeployinganAI/MLapplication.
• UnderstandingofAIagentsandfunction/toolcalling.

WhatWeLookFor

Wevaluecandidateswho:

• Understandconceptsratherthansimplycopyingcodefromtutorials.
• CanexplainhowtheirmodelsorAIworkflowswork.
• Arecomfortableexperimentingandevaluatingdifferentapproaches.
• Candebugproblemssystematically.
• UnderstandthatLLMoutputisnotautomaticallycorrect.
• Canidentifyhallucinations,poorretrieval,andincorrectmodelbehavior.
• Readtechnicaldocumentationindependently.
• HavebuiltAI/MLprojectsoutsidetheircoursework.
• Cancommunicatetechnicalfindingsclearly.

PreferredEducationalBackground

• B.Tech/B.E.–ComputerScience,AI/ML,DataScience,IT,orrelateddisciplines.
• BCA/MCA.
• M.Tech/M.Sc.inAI,ML,DataScience,ComputerScience,orrelatedfields.
• Final-yearstudentsandrecentgraduatesareencouragedtoapply.

PracticalEvaluation

ShortlistedcandidatesmaybegivenanAI/MLassignmentinvolvingoneormoreofthefollowing:

1. BuildabasicMLclassificationorregressionmodel.
2. Cleanandanalyzeaprovideddataset.
3. BuildasimpleRAG-baseddocumentQ&Aapplication.
4. IntegrateanLLMAPIintoaPythonapplication.
5. DesignapromptandevaluationapproachforanAItask.
6. IdentifyandimprovehallucinationsinanAI-generatedresponse.
7. BuildasimpleAIagentcapableofcallinganexternalAPI.

CandidatesmayalsobeaskedtoexplainapersonaloracademicAI/MLproject.

InternshipExpectations

Duringtheinternship,thecandidateisexpectedto:

• WorkonrealAI/productengineeringproblems.
• Writeandmaintainproduction-orientedcodeundersupervision.
• Conductexperimentsanddocumentresults.
• Participateintechnicaldiscussionsandcodereviews.
• InvestigateAI/MLfailuressystematically.
• Learnnewframeworksandtechnologiesasprojectrequirementsevolve.
• FollowGit,documentation,testing,anddevelopmentpractices.
• Demonstratemeasurableimprovementthroughouttheinternship.

PotentialConversion

High-performinginternsmaybeconsideredforaFull-TimeAI/MLEngineer,AIEngineer,orSoftwareEngineer–AIpositionbasedon:

• Technicalfundamentals
• Programmingability
• Problem-solving
• Qualityofimplementation
• AI/MLunderstanding
• Experimentationandevaluationskills
• Learningvelocity
• Ownership
• Contributiontoproductdevelopment