Job Description

Summary The Senior Generative AI Software Architect is a visionary leader responsible for
defining and delivering scalable, innovative AI solutions using cutting-edge
generative AI models. This role entails architecting systems that leverage
advanced AI to solve complex business problems and enable transformative
applications. You will support enterprise-scale AI initiatives leveraging Bedrock
foundational models, Azure OpenAI, and Google Gemini. The core platform is
based on AWS, with additional integrations into Azure for specific AI use cases.
The Senior Architect works closely with product owners, data scientists, and
software development teams to design frameworks, deploy models, and ensure
seamless integration with enterprise systems. As a technical authority, this role
emphasizes system scalability, high performance, security, and ethical
considerations. You will guide teams in adopting generative AI technologies,
mentor engineering teams, and drive innovation to deliver a competitive edge.
As a GE Vernova accelerator, GE Vernova Advanced Research is driving
strategy and leading research & development efforts to execute on the
business's mission to help power the energy transition. We forge the
collaborations and help invent the technologies required to electrify and
decarbonize for a zero-carbon future.
Representing virtually every major scientific and engineering discipline, our
researchers are collaborating with GE Vernova's businesses, the U.S.
government, and more than 420 entities at the forefront of technology to execute
on 150+ energy-focused projects. Collectively, these research programs and
initiatives aim to solve near term technical challenges, deliver next generation
product advances, and drive long term breakthrough innovation to enable more
affordable, reliable, sustainable, and secure energy.

Roles & Responsibilities:

• Architect and oversee the development of robust, scalable systems for deploying generative AI models
• Collaborate with stakeholders to define business requirements and technical specifications for generative AI applications.
• Guide the selection, customization, and optimization of state-of-the-art generative AI models.
• Develop end-to-end pipelines for model training, inference, and monitoring in production environments
• Ensure systems meet high standards for performance, scalability, and security while adhering to data privacy regulations.
• Lead the implementation of APIs, microservices, and frameworks to integrate AI models into enterprise solutions.
• Design scalable and efficient architectures for generative AI models, applications, and workflows.
• Ensure seamless integration of generative AI capabilities into existing systems. Provide mentorship to engineering teams, fostering expertise in AI and software architecture.
• Design and maintain platforms to handle large-scale solution applications in collaboration with legal and compliance teams
• Define architectural best practices to mitigate risks associated with Generative AI (e.g., model hallucinations)
• Align AI architecture with organizational goals and contribute to strategic technology roadmaps.
• Document architectural designs, workflows, and decisions for transparency and scalability.

• Strong knowledge of neural network architectures (e.g., CNNs, RNNs, Transformers).
• Expertise in designing scalable, distributed architectures for AI systems. Strong experience with cloud computing platforms (AWS, Azure, GCP) and containerization (Kubernetes, Docker).
• Familiarity with large-scale distributed systems and database technologies. Experience in creating technical design documents and implementation playbooks for target state AI solutions within cloud environments based on
• Experience translating business requirements into technical solution designs Thorough understanding of integration platforms and protocols (e.g., REST, SOAP, HTTP, UDP, ETC.)
• Proficiency in designing RESTful APIs and GraphQL endpoints for AI services.
• Knowledge of API development, microservices architecture, and DevOps practices. Proficiency in MLOps/LLMOps and model lifecycle management, including CI/CD pipelines for training, testing, and deploying AI models at scale.
• Expertise in prompt engineering and fine-tuning large language models (LLMs), including techniques like reinforcement learning from human feedback (RLHF) and parameter-efficient tuning (e.g., LoRA).
• Performance optimization for AI/ML workloads, including GPU/TPU acceleration, model quantization, pruning, and distillation.
• Observability & monitoring of AI pipelines, encompassing logging, tracing, and metrics to detect drift, anomalies, or performance bottlenecks.
• Security, Privacy, and Compliance knowledge, with an understanding of data governance (GDPR, HIPAA, SOC 2) and secure model serving

• Master's degree or higher in Computer Science/Engineering, AI, or related fields.
• 10+ years of experience in software architecture with a focus on AI/ML systems.
• Proven track record of designing scalable generative AI use case solutions.
• Exceptional leadership, strategic thinking, and problem-solving abilities.
• Excellent communication skills for engaging with stakeholders across technical and business domains.
• Must be willing to work out of an office located in Bangalore, India.