Trainee AI Engineer

EXPERIENCE
0-1 years

FUNCTIONALAREA
Development

As an AIEngineer, youllbepartofanext-generationengineeringteambuildingintelligent, autonomoussystemsthatcombine Machine Learning, Large Language Models(LLMs), and Agenticreasoningframeworks. Youllparticipateend-to-endfromdataingestionandmodeltraining to Retrieval-Augmented Generation(RAG), agentorchestration, anddeploymentofscalablemicroservices. Thisroledemandscuriosity, precision, andapassionforbridgingtraditional MLandmodern LLM-basedagenticarchitecturesintoproduction-gradesystems.
Key Responsibilities
• Designandimplement RAGpipelinesforlarge-scaledataretrievalandcontextualreasoning.

• Build, train, andfine-tune MLmodelsusing Scikit-learn, Tens or Flow, or Py Torchforstructuredandunstructureddatasets.

• Developmodular AImicroservices(Fast API/Flask)integratedwithvectordatabases(FAISS, Chrom a DB, Elasticsearch)andembeddingpipelines.

• Orchestratemulti-agentsystemsusing Lang Graph, Lang Chain, or Model Context Protocol(MCP)fordynamictaskexecution.

• Integrateandevaluate LLMAPIs(Open AI, Azure Open AI, Groq, Anthropic, etc.)forreasoning, generation, andworkflowautomation.

• Implementdata and MLpipelines(Airflow, Zen ML, Dagster)fortraining, evaluation, andinference.

• Developandexpose RESTor Web Socket APIsformodelandagentcommunication.

• Applypromptengineeringandrerankingstrategiestoimprovecontextualaccuracyandsystemreasoning.

• Workcloselywithdata, backend, and AIengineerstodeploy, scale, andmonitorintelligentmicroservicesintest/stagingenvironments.

• Contributetoshared AIinfrastructuremodules, ensuringreproducibility, maintainability, andperformance.

• Researchandprototypenewarchitectures, includingmultimodal RAG, structuredreasoning, orfine-tuningpipelines.

Required Technical Skills
• Strongunderstanding of RAGarchitecturedocumentchunking, embedding, retrieval, reranking, andsynthesis.

• Proficiency in Python, includinglibrarieslike Pandas, Numpy, and Scikit-learn.

• Experience with Fast APIor Flaskformicroservice and APIdevelopment.

• Hands-on with Lang Graph, Lang Chain, or MCPformulti-agentorchestration.

• Familiaritywithvectordatabases(FAISS, Chrom a DB, Weaviate, Elasticsearch).

• Good Workingknowledge of ML/DLframeworks(Tens or Flow, Py Torch).

• Understandingofmodelservingvi a RESTAPIs, batchjobs, orpipelines.

• Exposuretocontainerization(Docker)andscalablearchitecturepatterns.

• Familiarity with Git, MLflow, or DVCforversioning, tracking, andcollaboration.

• Awarenessofcloud AI/MLservices(Azure ML, AWSSage Maker, GCPVertex AI).

• Understandingofpromptevaluation, retrievalaccuracymetrics, andcontextualreasoningbenchmarks.

Secondary Skills
• Curiositytoexplore Agentic AIframeworksandautonomousreasoningsystems.

• Analyticalmindsetwithafocusondebugging, optimization, andscalability.

• Strongcommunicationanddocumentationskillsforcollaborativeworkflows.

• Innovativeandresearch-drivenapproachtowardnew AIarchitectures.

• Eagernesstoexperiment, iterate, andlearnfromproduction-levelsystems.

Become an Atid!

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