Generative AI Solution Architect

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. 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. This role involves working closely with business stakeholders, data scientists, and engineering teams to design 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), 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 .