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I will plan, build and deploy an ai or ml platform
Kai Balciunas
About this gig
Most AI integrations stop at a single API call. Production AI systems require a pipeline: document ingestion, extraction, model inference, output validation, monitoring, and automated retraining when model quality drifts. Building that correctly from the start is what separates a prototype from a system you can trust.
This gig delivers custom AI applications built on AWS using Bedrock, SageMaker, and the full supporting infrastructure. Builds include LLM-powered pipelines, XGBoost classification and scoring models, RAG systems with citation grounding, and agentic workflows with tool calling and human review gates.
All inference endpoints are monitored via CloudWatch and SageMaker Model Monitor. Retraining pipelines run automatically when drift is detected. Every resource is provisioned in Terraform across development, staging, and production environments.
Certifications: AWS Solutions Architect, ML Engineer, Developer, AI Practitioner, Terraform Associate.
Get to know Kai Balciunas
Kai Balciunas
AI Engineer
- FromUnited States
- Member sinceJun 2026
- Avg. response time1 hour
Languages
English, Spanish, Lithuanian
AWS certified AI Engineer and Cloud Architect with extensive experience in implementing cloud infrastructure and shipping production-grade ML & AI solutions within AWS. I specialize in building enterprise RAG workflows, fine-tuning LLMs, and deploying scalable serverless ML applications using Terraform and Python. I currently specialize in AI & ML product development for the legal industry and have a proven track record of delivering automated legal intelligence and high-performance data platforms. Working so closely with this heavily regulated industry makes me equipped to handle any project.
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FAQ
Who pays for the cloud infrastructure and services used during the build?
The client's payment method is placed on file with the cloud provider before any provisioning begins. All usage costs are billed directly to the client's account. There is no markup on cloud spend.
How do you approach security and data privacy across a project?
Security is designed in from day one. Every build includes encryption at rest and in transit, least-privilege IAM roles, secrets management, and audit logging. Compliance requirements like HIPAA are scoped and provisioned upfront.
How do you make architectural decisions around cost?
Cost architecture follows the client's priorities. High availability needs get redundant, always-on infrastructure. Cost-sensitive projects get serverless designs that charge only for actual usage. Tradeoffs are agreed upon during scoping.
What does the handoff process look like when the project is complete?**
The client receives full architecture documentation and maintenance tutorials written for whoever owns the system going forward. Once deliverables are confirmed, credentials and admin access are transferred to the designated party.
When does your access to the system end and how is that handled?**
Developer access is fully revoked after handoff. IAM roles are removed, temporary credentials are rotated, and cloud account access is relinquished. The client ends up with complete ownership and no residual developer access.
Will our team be able to maintain the system after delivery without deep cloud expertise?
Yes. Infrastructure is managed in Terraform so changes follow a predictable, code-driven process. Documentation and tutorials are written for the actual team taking ownership, not a hypothetical senior engineer.
How are scope changes handled mid-project?
Minor adjustments within the original scope are handled as part of normal delivery. Changes that materially expand scope are discussed openly and result in a revised timeline and cost estimate before any additional work begins.
Do you work with existing infrastructure or only greenfield builds?
Both. Greenfield builds start from scratch and are designed correctly from day one. Existing infrastructure engagements begin with an audit covering security, cost, and architecture before any changes are made to live systems.
