Job Title : Generative AI Architect

Location : Bengaluru (Local candidates only)

Experience : 10+ Years

Employment Type : Full-Time (Permanent)

Work Mode : Hybrid

About the Role :

We are seeking an experienced Generative AI Architect to lead the design, architecture, and implementation of enterprise-grade Generative AI solutions. The ideal candidate will possess deep expertise in Large Language Models (LLMs), AI solution architecture, machine learning, cloud technologies, and modern AI frameworks. You will work closely with business stakeholders, engineering teams, and data scientists to transform business challenges into scalable AI-powered solutions.

Key Responsibilities :

1. AI Solution Architecture :

- Design scalable, secure, and production-ready Generative AI architectures.

- Define end-to-end AI solution architecture from data ingestion to model deployment.

- Build reusable AI platforms and frameworks for enterprise-wide adoption.

- Select appropriate AI models and frameworks based on business use cases.

2. Generative AI & LLM Development :

- Design and develop applications powered by Large Language Models (LLMs).

- Integrate OpenAI, Azure OpenAI, Anthropic Claude, Gemini, Llama, or similar models into enterprise applications.

- Develop intelligent chatbots, AI assistants, document intelligence solutions, and automation platforms.

- Optimize prompts and implement prompt engineering best practices.

3. Retrieval-Augmented Generation (RAG) :

- Design RAG pipelines for enterprise knowledge retrieval.

- Build semantic search applications using embeddings.

- Integrate Vector Databases for contextual AI responses.

- Optimize retrieval accuracy and response quality.

4. AI Model Integration :

- Integrate AI models with enterprise applications using APIs.

- Develop microservices for AI inference.

- Work with REST APIs, SDKs, and AI orchestration frameworks.

- Ensure secure authentication and API management.

5. Data Engineering :

- Design data pipelines supporting AI applications.

- Process structured and unstructured enterprise data.

- Build document ingestion pipelines.

- Implement ETL workflows for AI training and inference.

6. Cloud & Infrastructure :

- Deploy AI applications on Azure, AWS, or Google Cloud Platform.

- Manage containerized deployments using Docker and Kubernetes.

- Optimize cloud infrastructure for scalability and cost efficiency.

- Implement monitoring and logging for AI services.

7. AI Governance & Security :

- Ensure Responsible AI practices.

- Implement data privacy and security standards.

- Define governance frameworks for AI model usage.

- Address AI bias, explainability, and compliance requirements.

8. Leadership & Innovation :

- Lead Proof of Concepts (POCs) and Minimum Viable Products (MVPs).

- Mentor engineering teams on AI best practices.

- Evaluate emerging AI technologies and recommend adoption strategies.

- Drive enterprise AI transformation initiatives.

Required Technical Skills :

1. Programming :

- Python (Expert)

- SQL

- REST APIs

- JSON

- Git

2. Generative AI :

- OpenAI GPT

- Azure OpenAI

- Anthropic Claude

- Google Gemini

- Meta Llama

- Prompt Engineering

- AI Agents

3. Frameworks :

- LangChain

- LangGraph

- LlamaIndex

- Semantic Kernel

- Haystack (Good to have)

4. Machine Learning :

- NLP

- Deep Learning

- Transformers

- Hugging Face

- TensorFlow or PyTorch

5. Vector Databases :

- Pinecone

- Weaviate

- ChromaDB

- FAISS

- Milvus

6. RAG :

- Retrieval-Augmented Generation

- Embeddings

- Semantic Search

- Knowledge Graph Integration

7. Cloud Platforms :

- Microsoft Azure

- Azure AI Studio

- Azure OpenAI Service

- AWS Bedrock

- Amazon SageMaker

- Google Vertex AI

8. DevOps & MLOps :

- Docker

- Kubernetes

- MLflow

- CI/CD Pipelines

- Model Monitoring

- Model Versioning

Required Experience :

- 10+ years in Software Engineering, AI, Machine Learning, or Solution Architecture.

- 3+ years of hands-on experience in Generative AI and LLM-based application development.

- Experience delivering enterprise AI solutions from concept to production.

- Strong experience integrating AI models into enterprise systems.

- Proven experience designing scalable cloud-native AI architectures.

Preferred Qualifications :

- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field.

- Certifications in Azure AI Engineer, AWS Machine Learning Specialty, Google Professional Machine Learning Engineer, or OpenAI technologies are preferred.

- Experience with enterprise digital transformation and AI modernization initiatives.

Soft Skills :

- Strong solution architecture and system design skills.

- Excellent stakeholder management and communication abilities.

- Analytical thinking and problem-solving mindset.

- Ability to lead cross-functional technical teams.

- Passion for innovation and emerging AI technologies.

Nice-to-Have :

- Experience with AI governance frameworks.

- Fine-tuning open-source LLMs.

- Multi-agent AI systems.

- Knowledge Graphs.

- GraphRAG implementations.

- AI observability tools (LangSmith, Weights & Biases).

- Experience in enterprise AI transformation projects.