Generative AI Solution Architect

As a Generative AI Solution Architect with 7+ years of experience, your role will involve designing, implementing, and scaling AI-powered solutions across enterprise use cases. You will work closely with business stakeholders, data scientists, and engineering teams to ensure the development of scalable, secure, and high-performing AI-driven applications. Key Responsibilities: - Architect end-to-end Generative AI solutions, integrating LLMs, vector databases, APIs, and cloud-native services. - Define system architecture, data flows, and integration strategies between AI models and existing enterprise platforms. - Ensure solutions are scalable, cost-efficient, secure, and aligned with compliance requirements. - Lead backend development for AI-driven applications using modern frameworks (e.g., Python, Node.js). - Build and optimize APIs, microservices, and middleware for serving and integrating AI models at scale. - Implement best practices for caching, asynchronous processing, distributed computing, and high availability. - Work with LLMs (e.g., GPT, Claude, LLaMA, Gemini) for fine-tuning and prompt engineering for domain-specific use cases. - Integrate vector databases (Pinecone, Weaviate, FAISS, Milvus, Redis) for semantic search, RAG (Retrieval-Augmented Generation), and personalization. - Evaluate, benchmark, and recommend models, frameworks, and tools suitable for enterprise applications. - Partner with data scientists, ML engineers, and product teams to translate business requirements into technical architectures. - Mentor development teams on backend and AI integration best practices. - Serve as a technical advisor in client or stakeholder discussions. Required Qualifications: - Bachelors or Masters degree in Computer Science, Software Engineering, or a related field. - 8+ years of experience in backend development with expertise in designing large-scale distributed systems. - Strong proficiency in RESTful APIs, GraphQL, microservices architecture, and event-driven systems. - Hands-on experience with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes). - Proven track record of integrating AI/ML solutions, ideally with Generative AI frameworks (LangChain, LlamaIndex, Hugging Face, OpenAI APIs). - Deep understanding of databases (SQL, NoSQL, graph, vector) and data pipelines. - Familiarity with MLOps practices (CI/CD for ML, model deployment, monitoring). - Experience with retrieval-augmented generation (RAG) pipelines. - Exposure to enterprise security, data governance, and compliance frameworks (e.g., GDPR, HIPAA). - Knowledge of DevOps, infrastructure-as-code (Terraform, CloudFormation). - Strong communication skills with the ability to engage both technical and non-technical stakeholders. - Prior experience as a solution architect, technical lead, or backend lead engineer.,