Design and deliver AWS-based integration architectures for Vantive’s clinical, operational, and commercial systems.
Compensation
The estimated base salary for this position is $124,000 to $170,000 annually. The estimated range is meant to reflect an anticipated salary range for the position. This position may also be eligible for discretionary bonuses.
Responsibilities
Design and implement AWS-based architectures for integration workloads connecting clinical, supply chain, and enterprise systems
Develop integration patterns including API design, event-driven messaging, and data synchronization across Vantive's application landscape
Contribute to architectural standards, design patterns, and technical documentation
Collaborate with senior architects to translate business and clinical requirements into technical solutions
Evaluate and recommend appropriate AWS services for specific use cases
Requirements
Bachelor’s degree in computer science, Engineering, or related field (or equivalent practical experience)
This postdoctoral fellowship is designed for a researcher at the intersection of industry-scale data and academic rigor. The role involves leading research in Document Understanding, designing and implementing AI-Native agentic systems to unlock insights from billions of historical and genealogical records.
Responsibilities
Innovate with State-of-the-Art AI for key Document Understanding tasks such as OCR/HTR, transcription, Named Entity Recognition (NER), Relation Extraction (RE), Coreference Resolution, Summarization, and Knowledge Graphs.
Design and implement multi-agent workflows using frameworks like LangChain, LangGraph, CrewAI, AutoGen, AgentCore, Strands, Google ADK, A2A.
Evaluate the performance of multi-modal AI & LLMs such as GPT, Gemini, Claude, Llama, and Qwen for zero-shot and few-shot scenarios.
We are seeking a highly skilled AI Engineer with 5+ years of overall experience in software development, Data Science, or Machine Learning to design, develop, and deploy cutting-edge AI systems leveraging Large Language Models (LLMs), Chatbots, Retrieval-Augmented Generation (RAG), and agentic AI architectures.
Responsibilities
Design, build, and deploy agentic AI systems using frameworks such as LangChain, LangGraph, and related libraries.
Develop and deploy multi-agent systems capable of autonomous decision-making, reasoning, planning, and collaboration.
Implement and optimize retrieval-augmented generation (RAG) systems, ensuring agents can access and incorporate external knowledge sources for grounded, accurate responses.
Fine-tune and prompt-engineer LLMs for task-specific reasoning, planning, and dynamic adaptation.
Work with LLM/SLM APIs, embeddings, and advanced generative AI techniques.
Lead the architecture, design, and development of advanced agentic applications using AI, GenAI, and autonomous agent paradigms across enterprise use cases.
Responsibilities
Lead the architecture, design, and development of advanced agentic applications using AI, GenAI, and autonomous agent paradigms across enterprise use cases.
Own end-to-end technical delivery within an Agentic Pod, ensuring solutions meet standards for scalability, resilience, security, and production readiness.
Architect intelligent systems that seamlessly combine LLMs, tools, APIs, data pipelines, and enterprise workflows.
Drive integration of agentic platforms with enterprise applications, APIs, data platforms, and cloud services.
Define and institutionalize design patterns, reusable components, accelerators, and reference architectures for agent-based systems.
Requirements
BE
B.Tech in Computer Science
Engineering
Nice to have: General Insurance domain (Health, Motor, Travel)
Lead the design and governance of enterprise-wide Generative AI architecture, defining strategy, standards, and best practices for scalable, secure, and responsible AI solutions across cloud-native environments.
Responsibilities
Define enterprise-wide Generative AI architecture strategy, standards, and best practices
Design scalable and secure LLM-powered solutions using Azure OpenAI and Azure AI Services
Architect enterprise-grade RAG platforms leveraging vector databases, semantic search, and knowledge management systems
Design and govern Agentic AI ecosystems utilizing AI Agents, tool calling, orchestration, and autonomous workflows
Evaluate and select appropriate foundation models, embeddings, and multimodal architectures
Requirements
Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or related field
Nice to have: Microsoft Certified: Azure Solutions Architect Expert
Nice to have: Microsoft Certified: Azure AI Engineer Associate
Nice to have: Microsoft Certified: Azure AI Fundamentals
Nice to have: Experience working with Fortune 500 enterprise AI transformation programs
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