VTal Technology Solutions
Architect - Generative AI, & Digital Automation, Integration & Automation
VTal Technology Solutions, New York, New York, us, 10261
Our client, a rapidly growing
innovator in the Lifesciences AI space , is seeking a talented
Gen AI Architect
to join their team onsite at the cutting-edge research facilities of a
leading global biopharmaceutical organization .
Overview Seeking a Gen AI Architect to lead the architecture & design of advanced AI/ML solutions, including agentic AI architectures and Management Control Protocols (MCPs). This strategic role involves creating scalable, modular, and production-grade AI frameworks, integrating modern architectural patterns, and ensuring seamless data integration across systems. The ideal candidate will have deep expertise in designing intelligent agent-based systems, microservices, and event-driven architectures, with strong hands-on knowledge in deploying enterprise-grade AI systems. This role requires a good understanding of architecture, AI/ML frameworks, Cloud, and modern automation tools, combined with a passion for solving complex problems.
Key Responsibilities
Architecture:
Create end-to-end technical architectures for applications utilizing generative AI (e.g., LLMs, GANs), intelligent document processing (e.g., OCR, NLP, data extraction), and workflow automation. Create end-to-end technical architectures for products leveraging generative AI (e.g., LLMs, GANs, Retrieval Augmented Generation - RAG), intelligent document processing (IDP) (e.g., OCR, NLP, data extraction), and workflow automation. Architect AI agent frameworks capable of autonomous decision-making, communication, and learning within complex environments, including the design of Management Control Protocols (MCPs). Apply modern architectural patterns such as microservices, event-driven architecture, and serverless design to build scalable and modular AI applications. Develop and oversee AI/ML training pipelines, inference engines, and deployment frameworks for both real-time (e.g., chatbots, content creation, synthetic data generation) and batch processing use cases. Define product architecture to integrate generative AI models (e.g., GPT-based models, diffusion models) into applications., including:
Foundation Models : Leverage large language models (LLMs) like GPT-4o, LLaMA, Grok, or BERT, and generative adversarial networks (GANs) such as Stable Diffusion or VAE-based systems for text, image, audio, or multimodal generation. Frameworks & Libraries : Utilize Hugging Face Transformers, LangChain, and DeepSpeed Orchestration & Pipelines : Tools like Apache Airflow, Kubeflow, or MLFlow Deployment Platforms : Cloud platforms (e.g., AWS, Azure )
Architect for real-time generative AI use cases (e.g., chatbots, content creation, synthetic data generation) and batch processing. Collaborate with product managers, developers, and stakeholders to translate business requirements into technical architecture. Document and present architecture, along with pros & cons to senior IT leadership Define and enforce AI/ML architecture standards, best practices, and design principles across the organization. Ensure the designed systems are secure, interpretable, scalable, and monitorable, and integrate well with CI/CD and MLOps pipelines.
Collaboration & Innovation :
Work closely with cross-functional teams (e.g., data science, DevOps, security) to ensure seamless deployment and operation of solutions. Identify opportunities to leverage emerging technologies to improve existing systems. Contribute to proofs-of-concept (PoCs) and pilot projects to validate new ideas.
Compliance & Security :
Ensure architecture complies with industry standards, data privacy regulations (e.g., GDPR, CCPA), and security best practices. Architect systems with robust authentication, encryption, and auditability features.
Qualifications
Experience & Technical Skill s:
10+ years of experience in production grade architecture Proven expertise in architecture with
generative AI technologies
(e.g., TensorFlow, PyTorch, Hugging Face Transformers). Strong background in automation technologies Proven experience agentic AI solutions or intelligent multi-agent systems. Deep understanding of architectural patterns including microservices, event-driven systems, and distributed computing. Experience architecting and managing Management Control Protocols (MCPs) or similar coordination frameworks in intelligent systems. Knowledge of data integration strategies, ETL/ELT processes, and data governance. Familiarity with knowledge graphs, semantic systems, or AI reasoning engines is a strong plus. Experience with cloud platforms (e.g., AWS, Azure, Google Cloud) and microservices architecture. Experience with enterprise-scale deployments in industries like finance, healthcare, life science. Familiarity with AI/ML frameworks, APIs, and model deployment (e.g., ONNX, Docker, Kubernetes). Experience in highly regulated or mission-critical domains (e.g., defense, healthcare, finance) where AI transparency and control are paramount.
Soft Skills :
Exceptional problem-solving and analytical skills. Strong communication skills to articulate complex technical concepts to non-technical stakeholders. Ability to work collaboratively in a fast-paced, agile environment.
innovator in the Lifesciences AI space , is seeking a talented
Gen AI Architect
to join their team onsite at the cutting-edge research facilities of a
leading global biopharmaceutical organization .
Overview Seeking a Gen AI Architect to lead the architecture & design of advanced AI/ML solutions, including agentic AI architectures and Management Control Protocols (MCPs). This strategic role involves creating scalable, modular, and production-grade AI frameworks, integrating modern architectural patterns, and ensuring seamless data integration across systems. The ideal candidate will have deep expertise in designing intelligent agent-based systems, microservices, and event-driven architectures, with strong hands-on knowledge in deploying enterprise-grade AI systems. This role requires a good understanding of architecture, AI/ML frameworks, Cloud, and modern automation tools, combined with a passion for solving complex problems.
Key Responsibilities
Architecture:
Create end-to-end technical architectures for applications utilizing generative AI (e.g., LLMs, GANs), intelligent document processing (e.g., OCR, NLP, data extraction), and workflow automation. Create end-to-end technical architectures for products leveraging generative AI (e.g., LLMs, GANs, Retrieval Augmented Generation - RAG), intelligent document processing (IDP) (e.g., OCR, NLP, data extraction), and workflow automation. Architect AI agent frameworks capable of autonomous decision-making, communication, and learning within complex environments, including the design of Management Control Protocols (MCPs). Apply modern architectural patterns such as microservices, event-driven architecture, and serverless design to build scalable and modular AI applications. Develop and oversee AI/ML training pipelines, inference engines, and deployment frameworks for both real-time (e.g., chatbots, content creation, synthetic data generation) and batch processing use cases. Define product architecture to integrate generative AI models (e.g., GPT-based models, diffusion models) into applications., including:
Foundation Models : Leverage large language models (LLMs) like GPT-4o, LLaMA, Grok, or BERT, and generative adversarial networks (GANs) such as Stable Diffusion or VAE-based systems for text, image, audio, or multimodal generation. Frameworks & Libraries : Utilize Hugging Face Transformers, LangChain, and DeepSpeed Orchestration & Pipelines : Tools like Apache Airflow, Kubeflow, or MLFlow Deployment Platforms : Cloud platforms (e.g., AWS, Azure )
Architect for real-time generative AI use cases (e.g., chatbots, content creation, synthetic data generation) and batch processing. Collaborate with product managers, developers, and stakeholders to translate business requirements into technical architecture. Document and present architecture, along with pros & cons to senior IT leadership Define and enforce AI/ML architecture standards, best practices, and design principles across the organization. Ensure the designed systems are secure, interpretable, scalable, and monitorable, and integrate well with CI/CD and MLOps pipelines.
Collaboration & Innovation :
Work closely with cross-functional teams (e.g., data science, DevOps, security) to ensure seamless deployment and operation of solutions. Identify opportunities to leverage emerging technologies to improve existing systems. Contribute to proofs-of-concept (PoCs) and pilot projects to validate new ideas.
Compliance & Security :
Ensure architecture complies with industry standards, data privacy regulations (e.g., GDPR, CCPA), and security best practices. Architect systems with robust authentication, encryption, and auditability features.
Qualifications
Experience & Technical Skill s:
10+ years of experience in production grade architecture Proven expertise in architecture with
generative AI technologies
(e.g., TensorFlow, PyTorch, Hugging Face Transformers). Strong background in automation technologies Proven experience agentic AI solutions or intelligent multi-agent systems. Deep understanding of architectural patterns including microservices, event-driven systems, and distributed computing. Experience architecting and managing Management Control Protocols (MCPs) or similar coordination frameworks in intelligent systems. Knowledge of data integration strategies, ETL/ELT processes, and data governance. Familiarity with knowledge graphs, semantic systems, or AI reasoning engines is a strong plus. Experience with cloud platforms (e.g., AWS, Azure, Google Cloud) and microservices architecture. Experience with enterprise-scale deployments in industries like finance, healthcare, life science. Familiarity with AI/ML frameworks, APIs, and model deployment (e.g., ONNX, Docker, Kubernetes). Experience in highly regulated or mission-critical domains (e.g., defense, healthcare, finance) where AI transparency and control are paramount.
Soft Skills :
Exceptional problem-solving and analytical skills. Strong communication skills to articulate complex technical concepts to non-technical stakeholders. Ability to work collaboratively in a fast-paced, agile environment.