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About Arun
GenAI Engineer and AI Solution Architect with 2+ years of experience designing and deploying
enterprise-scale Generative AI, Agentic AI, and RAG systems on AWS. Experienced in Amazon
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Bedrock, Claude models, MCP-based integrations, multi-agent architectures, document
intelligence platforms, and AI-driven cloud automation. Skilled in building secure, scalable,
production-ready AI systems with strong focus on governance, observability, compliance, and
business impact
Professional profile
- Location
- Chennai, India
- Time zone
- Asia/Calcutta
- Work mode
- Hybrid, On-site, Remote
- Availability
- Available Now
- AI experience
- 2+ years
- Industry experience
- 2+ years
- Languages
- English
- Working hours
- 09:00-18:00, Mon to fri
- Typical response
- Within 24 hours
- Travel
- Occasional travel
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Proven on the network
Implementation records and publicly available work, where published.
0 AI Deployments delivered. No publicly identified implementations are available to inspect.
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What Arun builds
Implementation domains listed on this profile. Evidence-supported items are identified separately below.
Specialisations
AI Agents
AI Cost Optimisation
Multi-Agent Systems
OCR & Intelligent Document Processing
RAG Systems
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AI Deployments
Real AI implementations demonstrated through evidence.
No publicly identified Deployments are available to inspect.
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Implementation capabilities
Evidence-supported means linked to a published Deployment or Case Study; other capabilities are Builder declared.
Evidence-supported
No published evidence linked yet.
Builder declared
- AI Agents
- AI Cost Optimisation
- Multi-Agent Systems
- OCR & Intelligent Document Processing
- RAG Systems
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Technology
Technologies listed by the Builder, distinguished by published evidence.
Evidence-supported
No published evidence linked yet.
Builder declared
- AWS
- AWS Rekognition
- AWS SES
- AWS Textract
- Amazon Bedrock
- Amazon Polly
- Amazon Q Developer
- Amazon S3
- Amazon Transcribe
- Anthropic Claude
- Chroma
- Clerk
- Cognito
- EasyOCR
- Elasticsearch Vector
- ElevenLabs
- FAISS
- LangChain
- LangGraph
- Llama 3.3
- Model Context Protocol
- Python
- pgvector
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Industry experience
Published Deployments support the listed counts. Other industries are Builder declared.
E-Commerce
Builder declared
FinTech
Builder declared
Healthcare Providers
Builder declared
Insurance
Builder declared
Logistics & Transportation
Builder declared
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Trust & verification
A closer look at how this profile and its evidence are reviewed.
Community Member
Published evidence is reviewed against the AI Deploy Network Evidence Standard.
How verification works
Verified Builder
Independently reviewed by AI Deploy Labs against the platform Evidence Standard.
How this is verifiedWhat the platform has established, and what remains builder-declared.
- Identity and organisation
- Identity checks confirm that the person presenting this profile controls the account and the professional identifiers attached to it, such as a LinkedIn profile. Organisation membership is confirmed by the organisation, not self-declared by the builder.
- AI Deployments
- An approved AI Deployment is an implementation record the builder submitted and the platform reviewed against the Evidence Standard before publication. Approval confirms the record is complete, coherent and consistent with the supporting material provided — it is not an audit of the client's systems.
- Business outcomes
- Outcome figures are recorded by the builder on the deployment or case study that produced them, and are reviewed at publication. Aggregates shown on this profile are calculated from those approved records only.
- Evidence
- Supporting evidence comes from the records the builder published on this platform: approved AI Deployments, published AI Case Studies, verification outcomes and organisation participation.
- Currency
- Older evidence continues to count and is never removed. Recency is presented separately through Professional Currency so that current expertise can be assessed alongside historical work.
- Traceability
- Every figure on this profile can be followed to the record that produced it. Open the linked AI Deployment or AI Case Study to read the outcome exactly as it was reported.
The platform does not independently audit client systems or financial records. How this is verified
Professional record and further evidence
Professional Certifications
Some recognitionAWS Agentic AI Demonstrated
AWS
Issued Jun 2026
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