Generative AI Solution Architect

Generative AI Solution Architect

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), 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

• Bachelor’s or Master’s 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

  .