Responsible for designing, developing, and deploying scalable Generative AI solutions using state-of-the-art large language models (LLMs) and transformer architectures.
- Responsible for building out sophisticated AI agent workflows, establishing a scalable cloud infrastructure, and ensuring the application can handle large- scale document ingestion and concurrent user requests seamlessly.
- Design and implement highly complex Retrieval-Augmented Generation (RAG) systems, specifically focusing on Agentic RAG (autonomous decision-making workflows) and Multimodal RAG (handling text, images, and other data types).
Backend Development & Orchestration: Build robust, high-performance APIs and backend services using Python and FastAPI. Orchestrate complex LLM workflows and agents utilizing LangChain and LangGraph.
- Search & Data Infrastructure: Develop data ingestion pipelines capable of handling exceptionally large documents. Configure, optimize, and maintain Vector Databases and Azure AI Search for low-latency, high-accuracy retrieval
- Frontend & UI Development: Develop rapid, functional, and interactive user interfaces using Streamlit. Utilize JavaScript to build custom frontend components, integrate web elements, and enhance the overall user experience.
- DevOps & Automation: Establish, maintain, and optimize CI/CD pipelines to ensure smooth, automated testing, building, and deployment of the GenAI application.
Document Processing: Handson in parsing complex financial documents, unstructured enterprise documents (PDFs, PPTs, tables, Excel, Word, txt, charts, images etc)
Required Skills
- Programming Languages: Deep expertise in Python; solid working knowledge of JavaScript.
- Strong experience with Azure OpenAI, Azure Machine Learning, Cognitive Search, and AI Studio.
- Solid understanding of LLMs, embeddings, RAG pipelines, vector search, prompt engineering.
- GenAI & LLM Frameworks: Extensive hands-on experience with LangChain and LangGraph/ Semantic Kernel / LlamaIndex. Proven ability to build beyond basic RAG into Agentic and Multimodal implementations.
- Backend & APIs: Strong experience building scalable RESTful APIs with FastAPI.
- Cloud & DevOps (Azure): Demonstrated experience deploying enterprise-grade applications on Azure. Deep understanding of Docker, containerization, and configuring automated CI/CD pipelines.
- LLM Evaluation & Observability: Hands-on experience with evaluation frameworks (e.g., RAGAS, TruLens, DeepEval) and monitoring/tracing tools (e.g., LangSmith, Langfuse, Arize Phoenix).
- Databases & Search: Practical experience with Azure AI Search and leading Vector Databases (e.g., Pinecone, Milvus, Qdrant, or Azure Native).
- System Architecture: Proven track record of designing systems for parallelism, large-scale data ingestion, and multi-user environments (SaaS architecture).
- Frontend/Prototyping: Experience building responsive interfaces using Streamlit,HTML,CSS,Javascript.
- Docker, Kubernetes (for model deployment)
- Previous experience building commercial AI products or complex advisory/analytical AI agents from the ground up.
- Strong understanding of data security and tenant isolation in cloud environments.
- Understanding of responsible AI, data privacy, and application security, Guardrails.
- Fine-tune and customize foundation models using domain-specific datasets and techniques.
Responsible for designing, developing, and deploying scalable Generative AI solutions using state-of-the-art large language models (LLMs) and transformer architectures.
- Responsible for building out sophisticated AI agent workflows, establishing a scalable cloud infrastructure, and ensuring the application can handle large- scale document ingestion and concurrent user requests seamlessly.
- Design and implement highly complex Retrieval-Augmented Generation (RAG) systems, specifically focusing on Agentic RAG (autonomous decision-making workflows) and Multimodal RAG (handling text, images, and other data types).
Backend Development & Orchestration: Build robust, high-performance APIs and backend services using Python and FastAPI. Orchestrate complex LLM workflows and agents utilizing LangChain and LangGraph.
- Search & Data Infrastructure: Develop data ingestion pipelines capable of handling exceptionally large documents. Configure, optimize, and maintain Vector Databases and Azure AI Search for low-latency, high-accuracy retrieval
- Frontend & UI Development: Develop rapid, functional, and interactive user interfaces using Streamlit. Utilize JavaScript to build custom frontend components, integrate web elements, and enhance the overall user experience.
- DevOps & Automation: Establish, maintain, and optimize CI/CD pipelines to ensure smooth, automated testing, building, and deployment of the GenAI application.
Document Processing: Handson in parsing complex financial documents, unstructured enterprise documents (PDFs, PPTs, tables, Excel, Word, txt, charts, images etc)
Required Skills
- Programming Languages: Deep expertise in Python; solid working knowledge of JavaScript.
- Strong experience with Azure OpenAI, Azure Machine Learning, Cognitive Search, and AI Studio.
- Solid understanding of LLMs, embeddings, RAG pipelines, vector search, prompt engineering.
- GenAI & LLM Frameworks: Extensive hands-on experience with LangChain and LangGraph/ Semantic Kernel / LlamaIndex. Proven ability to build beyond basic RAG into Agentic and Multimodal implementations.
- Backend & APIs: Strong experience building scalable RESTful APIs with FastAPI.
- Cloud & DevOps (Azure): Demonstrated experience deploying enterprise-grade applications on Azure. Deep understanding of Docker, containerization, and configuring automated CI/CD pipelines.
- LLM Evaluation & Observability: Hands-on experience with evaluation frameworks (e.g., RAGAS, TruLens, DeepEval) and monitoring/tracing tools (e.g., LangSmith, Langfuse, Arize Phoenix).
- Databases & Search: Practical experience with Azure AI Search and leading Vector Databases (e.g., Pinecone, Milvus, Qdrant, or Azure Native).
- System Architecture: Proven track record of designing systems for parallelism, large-scale data ingestion, and multi-user environments (SaaS architecture).
- Frontend/Prototyping: Experience building responsive interfaces using Streamlit,HTML,CSS,Javascript.
- Docker, Kubernetes (for model deployment)
- Previous experience building commercial AI products or complex advisory/analytical AI agents from the ground up.
- Strong understanding of data security and tenant isolation in cloud environments.
- Understanding of responsible AI, data privacy, and application security, Guardrails.
- Fine-tune and customize foundation models using domain-specific datasets and techniques.
BTech / Mtech / IT
KPMG – це міжнародна мережа фірм, що надають аудиторські, податкові та консультаційні послуги. В офісах KPMG у 143 країнах світу працюють понад 273,000 співробітників (FY23). Кожна фірма KPMG є незалежною юридичною особою і представляє себе як таку.
KPMG працює в Україні з 1992 року. KPMG в Україні надає аудиторські, податкові, бухгалтерські та консультаційні послуги для місцевих і міжнародних компаній. Нашою метою завжди було використання глобального інтелектуального потенціалу фірми в поєднанні з практичним досвідом наших українських професіоналів, щоб допомогти провідним компаніям досягти своїх цілей.
Офіси компанії знаходяться у Києві та Львові.
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KPMG is a global network of professional services firms providing audit, tax and advisory services. We operate in 143 countries and territories, and in FY23, collectively employed more than 273,000 people working in member firms around the world.
KPMG in Ukraine provides audit, tax, accounting and advisory services to local and international businesses. KPMG has been working in Ukraine since 1992, and our goal has always been to use the firm's global intellectual potential, combined with the practical experience of our Ukrainian professionals, to help leading companies to achieve their goals.
In Ukraine KPMG has its offices in Kyiv and Lviv.
