GenAI Solution Architect

Large Language Model (LLM)

Required Skills and Qualifications:

Engineering

• Hands on proficiency in Design and Architecture of the platforms
• Good awareness of the design patterns & implementation experience
• Handson Proficiency in JavaScript/TypeScript, Python, or Go.
• Experience with React or Angular on the frontend.
• REST/GraphQL API design and integration.
• Familiarity with event-driven architectures and async processing (Kafka, RabbitMQ,etc.).Microservices
• Strong with AWS/ Azure, containerization (Docker, Kubernetes), and Infrastructure-as Code .
• Must have worked on the production-ready solutions and be abreast with the scaling and complexity solutions involved in creating enterprise solutions.

LLM & AI Platforms

• Experience integrating LLM APIs (OpenAI, Claude, Mistral, Cohere, etc.).
• Knowledge of LangChain, LlamaIndex, or Semantic Kernel for orchestration.
• Hands-on with embeddings, vector databases (Pinecone, FAISS, etc.), and prompt engineering.
• Familiarity with AI/ML deployment platforms (SageMaker, Vertex AI, Hugging Face, etc.).

Preferred candidate profile

Architecture & Design

• Define robust, scalable, cloud-native, cloud agnostic architectures for AI platforms
• Architect and prototype AI systems/platforms
• Design and document system architecture (component diagrams, API contracts, deployment topology).
• Champion modern design patterns (microservices, micro frontends, event-driven, DDD, etc.).
• Build composable architecture patterns for integrating LLM APIs with microservices and UI layers.
• Create end-to-end architectures, build distributions, environments and deploy solutions

Hands-On Development

• Build and integrate reusable components, LLM-enabled APIs, and workflows with stateful orchestration where needed (e.g., using LangChain or semantic pipelines).
• Develop and iterate on PoCs and reference implementations of LLM-based features.
• Write maintainable, high-quality code in both frontend (React) and backend (Python ,Node.js, Java, Go) technologies.
• Build reusable UI components, design systems, and frontend microservices where applicable.
• Create proof-of-concepts and reference implementations to de-risk architectural decisions.
• Contribute to CI/CD automation, DevOps pipelines, and infrastructure-as-code setups (Terraform, Helm).

Frontend Engineering

• Architect and implement responsive UIs with state management (Redux, Zustand, or similar).
• Integrate RESTful and GraphQL APIs, implement frontend performance tuning and security best practices (XSS, CSP, etc.).
• Define and enforce front-end development standards and reusable design patterns.

LLM Engineering & Integration

• Integrate LLMs via OpenAI, Claude, Mistral, Vertex AI, or AWS Bedrock APIs.
• Apply techniques like

  prompt engineering, embeddings, RAG (retrieval-augmented generation), and fine-tuning

  where applicable.
• Design and implement

  vector database-backed search pipelines

  using Pinecone, FAISS, Weaviate, or Vespa.
• Collaborate with data scientists and MLOps teams to bring custom LLMs to production securely and reliably.