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