GenAI Architect

Key Responsibilities :

- Design and architect enterprise AI/ML and Generative AI solutions.

- Develop production-grade AI applications using Python, FastAPI/Flask, and REST APIs.

- Design and implement LLM, RAG, AI Agent, and Multi-Agent solutions.

- Build cloud-native AI platforms using Azure, AWS, and/or GCP.

- Implement MLOps and LLMOps frameworks, including CI/CD, monitoring, governance, and model lifecycle management.

- Integrate AI solutions with enterprise platforms such as ServiceNow, Jira, observability, and ITSM platforms.

- Design scalable and secure AI platforms and microservices architectures.

- Lead architecture and design reviews and establish technical best practices.

- Mentor AI/ML engineering teams and provide technical guidance.

- Develop AI-driven solutions for Infrastructure Services, Cloud Operations, AIOps, and Application Managed Services.

- Monitor AI solutions in production and ensure reliability, scalability, security, and performance.

- Collaborate with business, technology, infrastructure, and operations stakeholders to identify and implement AI use cases.

Mandatory Skills :

Programming & Development :

- Strong proficiency in Python.

- Hands-on experience with FastAPI/Flask and REST APIs.

- Strong knowledge of SQL.

- Experience developing production-grade AI applications and microservices.

AI/ML & GenAI :

- Strong understanding of Machine Learning, Deep Learning, and NLP.

- Hands-on experience with Large Language Models (LLMs).

- Strong knowledge of Prompt Engineering and RAG.

- Experience with LangChain and LlamaIndex.

- Strong understanding of Agentic AI and Multi-Agent architectures.

Cloud & Platform Engineering :

- Experience with Azure, AWS, and/or GCP.

- Hands-on experience with Azure OpenAI, AWS Bedrock, and/or Google Vertex AI.

- Strong knowledge of Docker, Kubernetes, and Microservices.

- Experience designing scalable and secure cloud-native architectures.

MLOps / LLMOps :

- Experience implementing MLOps/LLMOps practices.

- Knowledge of CI/CD, model deployment, monitoring, observability, and governance.

- Experience with AI platform lifecycle management and production operations.

Vector Databases :

- Experience with one or more vector databases such as :

1. Pinecone

2. Milvus

3. Weaviate

4. pgVector

5. FAISS

Preferred Experience :

- Experience in Infrastructure Services and Cloud Operations.

- Exposure to Application Managed Services (AMS).

- Experience implementing AIOps solutions.

- Knowledge of ITSM and ServiceNow.

- Experience working with enterprise AI platforms.

- Knowledge of observability and monitoring platforms.

- Experience integrating AI solutions with enterprise applications and operational systems.

Preferred Candidate Profile :

- Strong hands-on experience in AI Platform Architecture and Generative AI.

- Proven ability to take AI solutions from architecture and development through production deployment and support.

- Strong understanding of enterprise cloud architecture and AI infrastructure.

- Ability to lead technical discussions and mentor engineering teams.

- Strong problem-solving and analytical skills.

- Excellent communication and stakeholder-management skills.

- Candidates currently serving their notice period are also encouraged to apply.