Full Stack Developer :: Acon
KPMG Delivery Network India · Consulting & Professional Services
- India
- On-site
- Posted today
- Apply by 30 Sep
- Software Engineering
About the job
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