Senior Architect- Gen AI

Key Responsibilities As a Data Architect with Generative AI expertise, you will: Design and implementrobust data architectures that support AI and machine learning (ML) workloads, including Generative AI applications. Develop and optimizedata pipelinesfor training, validating, and deploying AI models efficiently and securely. Integrate AI frameworksinto data platforms, ensuring scalability, low latency, and high throughput. Collaboratewith data scientists, AI engineers, and stakeholders to align data strategies with business goals. Lead initiatives to ensuredata governance , security, and compliance standards (e.g., GDPR, CCPA) are met in AI-driven environments. Prototype and implementarchitectures that utilize generative models (e.g., GPT, Stable Diffusion) to enhance business processes. Stay up to date with the latest trends in Generative AI, data engineering, and cloud technologies to recommend and integrate innovations. Required Qualifications We’re looking for someone with: Abachelor’s degreein Computer Science, Data Engineering, or a related field (master’s preferred). 5+ yearsof experience in data architecture, with a focus on AI/ML-enabled systems. Hands-on experience withGenerative AI models(e.g., OpenAI GPT, BERT, or similar), including fine-tuning and deployment. Proficiency indata engineering tools and frameworks , such as Apache Spark, Hadoop, and Kafka. Deep knowledge ofdatabase systems(SQL, NoSQL) andcloud platforms(AWS, Azure, GCP), including their AI/ML services (e.g., AWS Sagemaker, Azure ML, GCP Vertex AI). Strong understanding ofdata governance ,MLOps , andAI model lifecycle management . Experience with programming languages such asPythonorRand frameworks likeTensorFloworPyTorch . Excellent problem-solving andcommunication skills , with a demonstrated ability to lead cross-functional teams. Preferred Skills Familiarity withLLM fine-tuning ,prompt engineering , andembedding models . Strong domain expertise in Life Science Industry Experience integrating generative AI solutions into production-level applications. Knowledge ofvector databases(e.g., Pinecone, Weaviate) for storing and retrieving embeddings. Expertise inAPIs for AI models , such as OpenAI API or Hugging Face Transformers.

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