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Huron
Huron
5001 – 10 000 Darbinieki
H1B vīzas sponsorsLoģistikaMārketingsVeselības aprūpe
Huron ir globāls konsultāciju uzņēmums, kas palīdz organizācijām risināt sarežģītus uzņēmējdarbības izaicinājumus, piedāvājot stratēģijas, darbības, konsultāciju, digitālos, tehnoloģiju un analītikas pakalpojumus. Uzņēmuma darbība aptver veselības aprūpi, izglītību, enerģētiku un finanšu pakalpojumus, kā arī pielāgotu atbalstu tādās jomās kā pārvaldītie pakalpojumi, ārpakalpojumi un līderības attīstība. Huron apvieno speciālistus, kuri koncentrējas uz praktiskiem un nozīmīgiem risinājumiem, kas atbilst katra klienta īpašajām vajadzībām, tādēļ uzņēmums ir saistošs profesionāļiem, kurus interesē konsultācijas, pārmaiņu īstenošana un uz datiem balstīta organizāciju izaugsme.
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Vakances uzņēmumā

AI Engineer, Generative AI and RAG — Huron, Bangalore Hybrid

Build machine learning, generative AI, and retrieval-augmented generation solutions for Huron. Develop agentic applications, cloud AI pipelines, APIs, and production systems.

INIndijaHibrīdiPilna slodzeVidējais līmenisVecākais speciālistsNepieciešamā Valoda: angļuMākslīgā intelekta inženierisMašīnmācīšanās inženieris
AWSAzureDockerFlaskMākonisPythonPyTorchScikit-LearnTensorFlow

Apraksts

  • Create AI systems with machine learning and generative AI methods, including prompt engineering, fine-tuning, embeddings, and RAG.
  • Build intelligent applications and agentic systems with tools such as LangChain, LangGraph, AutoGen, or CrewAI.
  • Design and deliver full RAG pipelines, from document ingestion and chunking through vector indexing, hybrid search, and contextual responses.
  • Create modular, documented APIs and service components with FastAPI, Flask, or similar frameworks.
  • Build and manage AI pipelines for ingestion, transformation, validation, training, inference, deployment, and knowledge retrieval.
  • Use AWS and Azure AI and machine learning services to support development and deployment.
  • Set up monitoring and evaluation for model accuracy, drift, retrieval quality, grounding, reliability, and task reasoning.
  • Improve production RAG systems for speed, scalability, cost, and response quality.
  • Work with software engineers, data scientists, solution architects, and business stakeholders on solution design and production handoff.
  • Contribute to experiments, proofs of concept, pilots, and scalable deployments.
  • Keep up with generative AI, AI agents, orchestration frameworks, RAG evaluation, and continuous improvement.

Prasības

  • Bring 4–6 years of practical experience in AI/ML development or intelligent application engineering.
  • Have strong Python skills and familiarity with modern AI/ML libraries such as scikit-learn, PyTorch, TensorFlow, or OpenAI APIs.
  • Have designed and deployed RAG systems using embeddings, vector databases such as FAISS, Pinecone, Chroma, or Azure AI Search, and retrieval optimization methods.
  • Have built APIs or backend services with FastAPI, Flask, or equivalent frameworks.
  • Be familiar with agent frameworks such as LangGraph or AutoGen and multi-agent orchestration patterns.
  • Know cloud platforms such as AWS or Azure and container tools such as Docker.
  • Have practical experience with cloud ML platforms such as Azure Machine Learning, AWS SageMaker, or Google AI/ML services.
  • Hold a bachelor’s degree in Computer Science, Engineering, Data Science, or a related field.
  • Experience with orchestration standards or tools such as MCP or agent routing protocols is a plus.
  • Experience with MLOps pipelines, CI/CD for model delivery, RAG evaluation tools such as RAGAS, or model monitoring is a plus.
  • Knowledge of AI system evaluation, observability, prompt performance testing, and grounding validation is a plus.
  • Be able to collaborate across functions, manage multiple projects, and communicate with technical and non-technical audiences.
  • Cloud certifications in AI/ML services from Azure, AWS, or Google Cloud are a plus.

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