Job Title

Job Role Overview:We are seeking an experienced Generative AI Solution Architect to join our team. This role requires a deep understanding of AI/ML models, especially generative AI technologies like GPT, DALL·E, Stable Diffusion, etc. The ideal candidate will be responsible for designing, developing, and deploying AI-driven solutions that leverage generative models to solve complex business challenges. You will work closely with cross-functional teams to align AI strategies with business objectives and drive innovation.Key Responsibilities:Strategic AI Solution Design: Collaborate with business leaders and product managers to understand business requirements, pain points, and opportunities where AI can deliver significant value. Design end-to-end AI/ML solution architectures, including data pipelines, model development frameworks, deployment strategies, and integration with existing enterprise systems. Develop architectural blueprints, technical specifications, and detailed design documents for AI/ML initiatives. Technology Selection & Evaluation: Research, evaluate, and recommend appropriate AI/ML technologies, platforms, frameworks, tools, and services (e.g., TensorFlow, PyTorch, scikit-learn, AWS SageMaker, Azure ML, Google AI Platform, MLOps tools). Make informed decisions regarding cloud, on-premises, or hybrid deployment models, considering scalability, performance, cost-effectiveness, security, and maintainability. Stay abreast of the latest advancements in AI/ML, Generative AI (LLMs, diffusion models), and related emerging technologies, assessing their potential impact and applicability. Technical Leadership & Guidance: Provide technical leadership and architectural guidance to data science, ML engineering, and software development teams throughout the entire AI/ML lifecycle (experimentation, development, deployment, monitoring). Ensure adherence to architectural principles, coding standards, best practices in model development, versioning, testing, and validation. Conduct architectural reviews and provide constructive feedback to ensure solution integrity and quality. Data Architecture & Management: Work closely with data engineers and data governance teams to design robust data architectures that support AI/ML initiatives, ensuring data quality, accessibility, security, and ethical handling. Understand and influence data collection, storage, processing, and feature engineering strategies relevant to AI/ML models. Scalability, Performance & Security: Design AI solutions that are highly scalable, performant, resilient, and secure, capable of handling large datasets and high inference volumes. Implement robust security measures and privacy-by-design principles in all AI architectures.Ethical AI & Compliance: Champion responsible AI practices, ensuring that all AI solutions are developed and deployed ethically, addressing considerations such as fairness, bias mitigation, transparency, and explainability. Ensure compliance with relevant data privacy regulations (e.g., GDPR, CCPA) and industry-specific standards.Stakeholder Communication: Effectively communicate complex technical AI concepts and architectural decisions to both technical and non-technical stakeholders (including executive leadership). Manage expectations, present progress, and articulate the business value and ROI of AI solutions.Required Qualifications:A bachelor's or master's degree or equivalent in computer science, Artificial Intelligence, or related field.10-12 years of experience in AI development and architecture, with a focus on generative AI solutions specifically focused on designing and implementing AI/ML solutions in an enterprise environment. Proven expertise in designing and deploying end-to-end machine learning pipelines. Strong understanding of various AI/ML techniques and algorithms (e.g., supervised, unsupervised, reinforcement learning, deep learning, NLP, computer vision). Hands-on experience with at least one major cloud platform's AI/ML services (AWS, Azure, GCP). Proficiency in programming languages commonly used in AI/ML (e.g., Python, R, Java). Familiarity with MLOps principles and tools for continuous integration, deployment, and monitoring of ML models. Solid understanding of data governance, data quality, and data security principles. Excellent problem-solving, analytical, and critical thinking skills. Strong communication, presentation, and interpersonal skills, with the ability to influence and collaborate effectively across diverse teamsRequired Skills: Experience with Generative AI models (LLMs, diffusion models) and related frameworks (e.g., LangChain, LlamaIndex). Experience with big data technologies (e.g., Spark, Hadoop, Kafka). Certifications in relevant cloud AI/ML platforms (e.g., AWS Certified Machine Learning Specialty, Google Cloud Professional Machine Learning Engineer). Experience with agile development methodologies