„Huron“ yra pasaulinė konsultacijų įmonė, padedanti organizacijoms spręsti sudėtingus verslo iššūkius pasitelkiant strategijos, veiklos, konsultavimo, skaitmeninius, technologijų ir analitikos sprendimus. Jos veikla apima sveikatos priežiūros, švietimo, energetikos ir finansinių paslaugų sektorius, o pritaikyta pagalba teikiama tokiose srityse kaip valdomosios paslaugos, paslaugų perdavimas išorės tiekėjams ir lyderystės ugdymas. „Huron“ suburia specialistus, orientuotus į praktiškus ir didelį poveikį darančius sprendimus, pritaikytus individualiems kiekvieno kliento poreikiams, todėl įmonė aktuali konsultavimu, transformacija ir duomenimis grįstu organizacijų augimu besidomintiems specialistams.
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.
Aprašymas
- 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.
Reikalavimai
- 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.