Senior R&D; AI & Cloud Solutions Architect

Position Overview

We are looking for a highly skilled and innovation-driven Senior R&D; AI & Cloud Solutions Architect to lead the research, design, development, and implementation of next-generation AI and cloud solutions. The role will focus on identifying emerging technologies, building AI-powered products and platforms, developing proof-of-concepts, and converting innovative ideas into scalable enterprise solutions.

The ideal candidate will have strong hands-on experience across Artificial Intelligence, Generative AI, Machine Learning, Cloud Architecture, APIs, Data Platforms, and DevOps , combined with the ability to work in an R&D; environment and translate business problems into technology solutions.

Key ResponsibilitiesAI & Generative AI R&D;

- Research and evaluate emerging AI, Generative AI, LLM, NLP, computer vision, and intelligent automation technologies.
- Design and develop AI/ML and GenAI-based solutions for enterprise use cases.
- Develop and evaluate LLM applications, AI agents, RAG architectures, vector databases, prompt engineering, and model orchestration .
- Evaluate commercial and open-source AI models and frameworks.
- Build rapid prototypes and Proofs of Concept (PoCs) to validate new AI capabilities.
- Assess model performance, accuracy, scalability, security, and cost.
- Explore opportunities to integrate AI into existing enterprise applications and workflows.

Cloud Architecture & Engineering
- Design scalable, secure, resilient, and cost-effective cloud architectures across AWS, Microsoft Azure, and/or Google Cloud Platform .
- Develop cloud-native AI and data solutions using services such as compute, containers, serverless, databases, storage, networking, and managed AI/ML platforms.
- Design hybrid and multi-cloud solutions where required.
- Establish cloud architecture standards, reference architectures, and technology patterns.
- Ensure solutions comply with enterprise security, governance, availability, and data protection requirements.

R&D; and Innovation
- Identify emerging technologies and assess their potential business value.
- Lead technology experiments, prototypes, technical evaluations, and innovation initiatives.
- Conduct technology/vendor evaluations and recommend appropriate platforms and frameworks.
- Maintain a technology roadmap covering AI, cloud, automation, data, and emerging technologies.
- Convert successful PoCs into production-ready solutions in collaboration with engineering and product teams.
- Stay current with developments in AI, cloud computing, cybersecurity, data engineering, and software architecture.

Solution Development




- Work closely with business stakeholders, product teams, architects, developers, data scientists, and infrastructure teams.
- Translate business requirements into technical architecture and solution designs.
- Develop APIs, microservices, AI services, integrations, and cloud applications.
- Establish development standards for AI and cloud solutions.
- Ensure solutions are scalable, maintainable, observable, and production-ready.
- Support troubleshooting and optimization of AI/cloud solutions.

MLOps / DevOps
- Implement CI/CD pipelines for AI and cloud applications.
- Establish MLOps practices covering model development, deployment, monitoring, versioning, and lifecycle management.
- Implement Infrastructure as Code using technologies such as Terraform, CloudFormation, or Bicep .
- Work with containerization and orchestration technologies such as Docker and Kubernetes .
- Implement monitoring, logging, performance management, and cost optimization.

AI Governance & Security
- Incorporate responsible AI principles into solution design.
- Address data privacy, model security, access control, auditability, and AI governance requirements.
- Evaluate risks associated with GenAI, including hallucination, prompt injection, data leakage, model misuse, and unauthorized access.
- Develop appropriate guardrails and validation mechanisms for enterprise AI solutions.

Required Technical SkillsArtificial Intelligence / GenAI
- Generative AI and Large Language Models
- OpenAI / Azure OpenAI or equivalent LLM platforms
- RAG and vector search
- Prompt engineering
- AI agents and agentic workflows
- NLP and Machine Learning fundamentals
- Python
- AI frameworks such as LangChain, LlamaIndex, Semantic Kernel, or equivalent
- Vector databases such as Pinecone, Weaviate, Milvus, or Azure AI Search

Cloud Strong experience with one or more:
- Microsoft Azure
- Amazon Web Services (AWS)
- Google Cloud Platform (GCP)

Preferred exposure to:
- Azure OpenAI / Azure AI Foundry
- AWS Bedrock
- Amazon SageMaker
- Google Vertex AI
- Cloud Functions / Lambda / Azure Functions
- Kubernetes
- Docker

Software Engineering
- Python and/or Java
- REST APIs and microservices
- SQL and NoSQL databases
- Git and GitHub/GitLab/Azure DevOps
- CI/CD
- Agile/Scrum methodologies




- API gateways and integration platforms

Data & Analytics
- Data engineering concepts
- Data pipelines and ETL/ELT
- Data lakes and data warehouses
- SQL
- Real-time data processing
- Data governance and quality

Preferred Certifications
- AWS Certified Solutions Architect
- Microsoft Azure Solutions Architect Expert
- Google Professional Cloud Architect
- Azure AI Engineer Associate
- AWS Machine Learning / AI certification
- Google Professional Machine Learning Engineer
- Relevant Kubernetes, DevOps, or security certifications

Education
- Bachelor's or Master's degree in Computer Science, Information Technology, Artificial Intelligence, Engineering, Data Science , or a related discipline.
- Advanced qualifications in AI, Cloud Computing, or Machine Learning will be an advantage.

Experience
- 8–12+ years of experience in technology/software engineering, cloud, architecture, AI/ML, or related fields.
- Minimum 3–5 years of hands-on experience with cloud technologies .
- Strong practical experience in AI/ML and preferably Generative AI/LLM solutions .
- Demonstrated experience taking technology concepts from research/PoC through production implementation .
- Experience working in an enterprise R&D;, innovation, architecture, or technology transformation environment.

Key Competencies
- Solid analytical and problem-solving skills
- Innovation and research mindset
- Excellent architecture and system-design capabilities
- Ability to rapidly prototype and experiment
- Strong programming and technical implementation skills
- Commercial awareness and ability to assess technology ROI
- Excellent stakeholder and communication skills
- Ability to explain complex AI/cloud concepts to technical and non-technical audiences
- Strong documentation and presentation skills
- Ability to work independently while collaborating across global teams

Key Performance Indicators
- Number and quality of AI/cloud PoCs successfully developed
- Conversion of successful PoCs into production solutions
- Innovation pipeline and technology roadmap execution
- Reduction in operational costs through AI/cloud automation
- Solution scalability, reliability, security, and performance
- Adoption of current AI/cloud capabilities across the organization
- Time taken from concept to working prototype
- Technology modernization and cloud optimization outcomes

Role Profile Job Title: Senior R&D; AI & Cloud Solutions Architect

Department: R&D; / Technology Innovation

Function: Artificial Intelligence, Cloud & Emerging Technologies

Experience: 8–12+ Years

Employment Type: Full-Time

Location: [Gurgaon / Hybrid]

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