Generative AI Architect

Description :

Job Summary :

We are seeking an experienced GenAI Architect to lead the design, development, and implementation of enterprise-grade Generative AI solutions. The ideal candidate will possess deep expertise in AI/ML, LLMs, vector databases, prompt engineering, and cloud-native architectures. The role involves working closely with engineering, product, data, and business teams to conceptualize and deploy scalable GenAI applications that drive business innovation and operational efficiency.

Key Responsibilities :

- Define and own the GenAI architecture strategy, solution roadmap, and reference architectures for enterprise-level implementations.

- Design, build, and deploy GenAI and LLM-based applications, including chatbots, copilots, automation systems, content generation modules, and knowledge intelligence platforms.

- Evaluate and leverage LLMs (open-source and commercial) such as OpenAI, Google Gemini, Meta Llama, Anthropic Claude, etc., based on business needs.

- Architect RAG (Retrieval-Augmented Generation) pipelines with vector databases (Pinecone, Chroma, Weaviate, Milvus, Redis Vector, etc.).

- Lead MLOps and LLMOps implementation for continuous model deployment, monitoring, and optimization.

- Optimize model performance via fine-tuning, prompt engineering, PEFT, quantization, and model distillation.

- Work with data engineers to build data pipelines and embeddings for AI workloads.

- Ensure security, compliance, and responsible AI practices, including data privacy, explainability, and bias mitigation.

- Collaborate with cross-functional teams to define use cases and translate them into scalable GenAI solutions.

- Develop and maintain technical documentation, PoCs, solution demos, and architectural presentations.

- Provide technical leadership and mentoring to AI engineers, developers, and data scientists.

Required Skills & Experience :

- Proven experience architecting and deploying Generative AI / LLM-based solutions.

- Expertise in Python, AI/ML frameworks, and GenAI libraries (Transformers, LangChain, LlamaIndex, etc.).

- Hands-on experience with cloud services for AI (Azure OpenAI, AWS Bedrock, GCP Vertex AI, etc.).

- Strong understanding of vector databases, embeddings, tokenization, and semantic search.

- Experience in RAG workflows, fine-tuning LLMs, and prompt engineering patterns.

- Familiarity with MLOps / LLMOps tools (MLflow, Kubeflow, Airflow, Ray, DVC, Weights & Biases, etc.).

- Strong understanding of microservices, containerization, and DevOps (Docker, Kubernetes, CI/CD).

- Excellent communication and stakeholder management skills.