AI Engineer

Akino Labs; Vadodara; Hybrid; INR 1,200,000 - 2,400,000; Full time

Job summary

Akino Labs in Vadodara is seeking an AI Engineer to design, develop, and deploy AI-powered capabilities across products and client solutions in a hybrid work setup.

The role focuses on Generative AI, LLM applications, RAG, AI agents, intelligent automation, model evaluation, and AI-enabled software engineering, collaborating with PMs, engineers, data/MLEs, QA and stakeholders to translate real business needs into reliable AI-powered solutions.

Qualifications

• 1–3 years of experience in AI Engineering, MLEngineering, Software Engineering with AI specialization, or a related role.
• Strong Python programming skills.
• Good understanding of software engineering fundamentals.
• Hands-on experience with LLMs and Generative AI.
• Experience integrating AI APIs.
• Understanding of REST APIs and backend development.
• Understanding of embeddings and vector search.
• Practical knowledge of RAG architectures.
• Strong debugging and problem-solving ability.
• Experience with Git/GitHub.

Responsibilities

• Design and develop production-ready AI-powered applications.
• Integrate AI capabilities into web, mobile, and enterprise apps.
• Build intelligent workflows for real-world business use cases.
• Develop reusable AI components, services, and APIs.
• Collaborate with teams to integrate AI into existing products.
• Evaluate new AI technologies and assess their applicability.
• Design and implement RAG pipelines and vector databases.
• Develop AI agents capable of multi-step workflows and tool use.
• Build AI Backend services, REST APIs, and deployment pipelines.
• Develop data/document processing pipelines and ensure reliability.

Skills

Python

LLMs

APIs

REST APIs

Embeddings

Vector search

RAG architectures

AI agents

Git/GitHub

Debugging

Education

B.Tech/B.E. in CS/AI/IT

MCA/M.Tech

M.Sc. in AI/ML/Data Science

Tools

Docker

Kubernetes

AWS

Azure

GCP

PostgreSQL/pgvector

Job description

Position:AIEngineer

Department:AI/Technology/ProductEngineering

Location:Vadodara/Hybrid

EmploymentType:Full-Time

Experience:1–3Years

AbouttheRole

WearelookingforanAIEngineertodesign,develop,integrate,anddeployAI-poweredcapabilitiesacrossourproductsandclientsolutions.

TherolefocusesonGenerativeAI,LLMapplications,RAG,AIagents,intelligentautomation,modelevaluation,andAI-enabledsoftwareengineering.

YouwillworkcloselywithProductManagers,SoftwareEngineers,Data/MLEngineers,QAEngineers,andbusinessstakeholderstoconvertreal-worldbusinessrequirementsintoreliableAI-poweredsolutions.

TheidealcandidateshouldbeastrongprogrammerwhounderstandsthatproductionAIrequiresmorethancallinganLLMAPI—itrequiresevaluation,reliability,datahandling,observability,security,andintegrationwithexistingsoftwaresystems.

KeyResponsibilities

AIApplicationDevelopment

• Designanddevelopproduction-readyAI-poweredapplications.
• IntegrateAIcapabilitiesintoweb,mobile,andenterpriseapplications.
• Buildintelligentworkflowsforreal-worldbusinessusecases.
• DevelopreusableAIcomponents,services,andAPIs.
• WorkwithengineeringteamstointegrateAIfunctionalityintoexistingproducts.
• EvaluatenewAItechnologiesanddeterminetheirpracticalapplicability.

GenerativeAI&LLMs

• DevelopapplicationsusingLargeLanguageModels(LLMs).
• WorkwithmodelsandAPIsfromproviderssuchas:
  • OpenAI
  • GoogleGemini
  • AnthropicClaude
  • Open-sourcemodels
• Implement:
  • Textgeneration
  • Classification
  • Summarization
  • Informationextraction
  • Questionanswering
  • ConversationalAI
  • Structureddatageneration
  • AI-assistedworkflows
• Designpromptsandsysteminstructionsforreliableoutputs.
• Implementstructuredoutputandvalidationmechanisms.

Retrieval-AugmentedGeneration(RAG)

• DesignandimplementRAGpipelines.
• Workwith:
  • Documentingestion
  • Chunking
  • Embeddings
  • Vectorsearch
  • Semanticsearch
  • Metadatafiltering
  • Retrievalandreranking
• Integratevectordatabasessuchas:
  • Pinecone
  • Qdrant
  • Weaviate
  • Chroma
  • pgvector
• Improveretrievalqualityandreduceirrelevantcontext.
• Developapproachesforhandlinglargeandcomplexdocumentcollections.

AIAgents&Automation

• DesignanddevelopAIagentscapableofexecutingmulti-stepworkflows.
• Implementtool/functioncalling.
• IntegrateAIagentswithexternalAPIs,databases,andbusinesssystems.
• Buildagentworkflowsfor:
  • Research
  • Dataprocessing
  • Customersupport
  • Documentprocessing
  • Businessautomation
  • Internaloperations
• Implementappropriateguardrailsandvalidationmechanisms.
• Monitorandimproveagentreliability.

AI/MLModelDevelopment

• Applymachine-learningtechniqueswhereappropriate.
• Performdatapreprocessingandfeatureengineering.
• Train,evaluate,andoptimizeMLmodelswhenrequired.
• Selectappropriatemodelsbasedonbusinessrequirementsandconstraints.
• WorkwithbothproprietaryAPIsandopen-sourcemodels.
• Understandthetrade-offsbetweenmodelquality,latency,cost,andinfrastructurerequirements.

AIEvaluation

• DesignevaluationframeworksforAIsystems.
• Createrepresentativetestdatasetsandevaluationscenarios.
• Measure:
  • Accuracy
  • Relevance
  • Groundedness
  • Consistency
  • Latency
  • Cost
  • Failurerate
• Identifyhallucinationsandothermodelfailuremodes.
• Comparedifferentmodels,prompts,retrievalstrategies,andsystemconfigurations.
• Implementautomatedevaluationwhereverpractical.

AIAPIs&BackendEngineering

• BuildAIservicesusingPythonandappropriatebackendframeworks.
• DevelopRESTAPIsandintegrations.
• IntegrateAIserviceswithexistingapplicationbackends.
• Handleauthentication,ratelimits,retries,logging,anderrorhandling.
• Buildasynchronousworkflowsforlong-runningAIoperations.

Data&DocumentIntelligence

• Buildsystemscapableofprocessingstructuredandunstructureddata.
• Workwithdocumentssuchas:
  • PDFs
  • Worddocuments
  • Excelfiles
  • Images
  • Webpages
  • Textdata
• Developextraction,classification,summarization,andanalysispipelines.
• Implementdocumentparsingandpreprocessingworkflows.

AIInfrastructure&Deployment

• DeployAIservicestocloudorproductionenvironments.
• Workwithtechnologiessuchas:
  • Docker
  • AWS
  • Azure
  • GCP
  • Serverlessinfrastructure
• OptimizeinferenceandAPIperformance.
• Implementlogging,monitoring,andobservability.
• Managemodel/APIconfigurationandenvironmentvariablessecurely.
• AssistinbuildingCI/CDworkflowsforAIservices.

Security&ResponsibleAI

• ImplementappropriatesecuritycontrolsaroundAIapplications.
• Protectsensitivedataandcredentials.
• PreventunauthorizedaccesstoAItoolsandbusinesssystems.
• Applyinput/outputvalidation.
• Designsafeguardsagainstpromptinjectionandmaliciousinputs.
• EnsureAIsystemsdonotexposeconfidentialinformationthroughgeneratedresponses.
• Followorganizationaldata-securityandprivacyrequirements.

RequiredSkills

• 1–3yearsofexperienceinAIEngineering,MLEngineering,SoftwareEngineeringwithAIspecialization,orarelatedrole.
• StrongPythonprogrammingskills.
• Goodunderstandingofsoftwareengineeringfundamentals.
• Hands-onexperiencewithLLMsandGenerativeAI.
• ExperienceintegratingAIAPIs.
• UnderstandingofRESTAPIsandbackenddevelopment.
• Understandingofembeddingsandvectorsearch.
• PracticalknowledgeofRAGarchitectures.
• Strongdebuggingandproblem-solvingability.
• ExperiencewithGit/GitHub.

GoodtoHave

• PyTorch
• TensorFlow
• Scikit-learn
• HuggingFaceTransformers
• LangChain
• LlamaIndex
• FastAPI
• PostgreSQL/pgvector
• MongoDB
• Redis
• Docker
• Kubernetes
• AWS/Azure/GCP
• MLflow
• LangSmithorsimilarobservability/evaluationplatforms
• Experiencewithopen-sourceLLMs
• Experiencewithfine-tuningorparameter-efficientfine-tuning
• ExperiencewithmultimodalAI
• Experiencewithspeech-to-text/text-to-speechsystems
• ExperiencebuildingAIagents
• ExperiencewithAIsecurityandguardrails

AIEngineeringMindset

Wearelookingforengineerswhounderstandthat:

• AnLLMAPIcallisnot,byitself,anAIproduct.
• AIoutputsneedmeasurableevaluation.
• Highermodelcapabilitydoesnotautomaticallymeanabetterproductionsolution.
• Cost,latency,reliability,privacy,andmaintainabilitymatteralongsideaccuracy.
• RAGqualitydependsheavilyondatapreparationandretrievalstrategy.
• AIagentsrequireappropriatetoolpermissionsandfailurehandling.
• AIsystemsshouldbetestedagainstrealisticfailurescenarios,notonlysuccessfulexamples.

CandidateProfile

Theidealcandidate:

• CanindependentlyresearchandimplementunfamiliarAItechnologies.
• Canmovefromprototypetoproduction-orientedimplementation.
• UnderstandsbothAIconceptsandconventionalsoftwareengineering.
• Canevaluatecompetingtechnicalapproachesobjectively.
• Iscomfortabledebuggingmodel,data,API,andapplication-levelfailures.
• Writesclean,maintainablePythoncode.
• CanexplaincomplexAIconceptstonon-technicalstakeholders.
• TakesownershipofAIfeaturesfromexperimentationthroughdeployment.

PracticalEvaluation

Shortlistedcandidatesmaybegivenatechnicalassignmentinvolvingoneormoreofthefollowing:

1. BuildaRAGapplicationoveradocumentcollection.
2. IntegrateanLLMAPIintoabackendservice.
3. DevelopanAI-poweredinformationextractionworkflow.
4. BuildabasicAIagentwithtool/functioncalling.
5. CreateanevaluationframeworkforanLLMapplication.
6. IdentifyandmitigatehallucinationsinasampleAIsystem.
7. Comparetwomodelsorpromptingstrategiesusingdefinedevaluationcriteria.
8. DesignanarchitectureforanAI-poweredbusinessworkflow.

CandidatesmayalsobeaskedtoexplainanAIprojecttheyhavepreviouslybuilt.

KeyPerformanceAreas

Performancewillbeevaluatedbasedon:

• QualityandreliabilityofAIimplementations
• AIevaluationmethodology
• Codequality
• Model/APIintegration
• Systemperformance
• Costandlatencyoptimization
• Problem-solvingability
• Productionreadiness
• Documentation
• Technicalownership
• ContributiontoAIproductdevelopment

PreferredEducationalBackground

• B.Tech/B.E.–ComputerScience,AI/ML,DataScience,IT,orrelateddiscipline
• MCA/M.Tech
• M.Sc.–AI,ML,DataScience,ComputerScience,orrelatedfield

Equivalentprofessionalexperienceanddemonstratedtechnicalcapabilitymaybeconsidered.