Senior Full Stack AI Engineer

Data Science UA is a service company with deep expertise in AI and Data Science. Our story started in 2016 with the first Data Science UA Conference in Kyiv, and since then, we’ve built one of the largest AI communities in Europe.

About the client:

Our client is a global Solution Integrator specializing in enterprise-grade infrastructure, platform engineering, and cloud-native architectures. They partner with the world's leading technology vendors to help organizations design and implement transformative solutions that drive innovation, efficiency, and growth. 

About the role: 

We are looking for a Full Stack Engineer to join the CTO Office and take ownership of our client’s product development lifecycle. You'll work hand-in-hand with the CTO Office team, taking initiatives that have been validated as POCs and leading them into production-grade, scalable solutions.

Responsibilities:

- Take validated POCs from the CTO Office and architect, build, and ship them as production-ready applications

- Design and develop full-stack solutions — modern frontends paired with robust, scalable backends

- Build and deploy within the AWS ecosystem (Lambda, ECS/EKS, DynamoDB, S3, RDS, API Gateway, CDK/CloudFormation, and more)

- Own the CI/CD pipeline — design, build, and maintain automated workflows using GitHub Actions

- Design and build internal agentic AI solutions using LangChain and LangGraph — from multi-step agent orchestration to RAG pipelines and tool-augmented workflows

- Leverage Agentic IDE tools (Cursor, Claude Code, and similar) as a core part of your development workflow, applying best practices around skills, MCPs, agents, and prompt engineering to accelerate delivery

- Collaborate closely with the CTO Office, product stakeholders, sales, and pre-sales teams to translate business needs into elegant technical solutions 

- Make pragmatic technology decisions that balance speed, quality, and maintainability

Requirements:

Must-Have:

- 5+ years of professional full-stack development experience

- Frontend: Strong proficiency with Vue.js or React (with modern tooling such as Nuxt/Next.js, TypeScript, Vite, Tailwind CSS)

- A builder's mindset — you thrive on taking something from prototype to production

- Understanding of system design principles — you can architect scalable, reliable distributed systems and make sound trade-offs

- Grasp of data structures and design patterns — to work alongside your Agentic IDE you write efficient, well-structured code

- Backend: Solid experience with Node.js, Python

- Hands-on experience with the AWS cloud ecosystem — you've built and deployed real workloads on AWS

- Hands-on experience building agentic AI systems with LangChain and LangGraph — including multi-agent orchestration, tool use, and RAG patterns

- Proven experience designing and maintaining CI/CD pipelines with GitHub Actions including automated PR based Code Reviews.

- Working knowledge of Agentic IDEs and AI-assisted development (Cursor, Claude Code, Windsurf, or similar) — including familiarity with skills, MCP servers, agents, and AI-driven development best practices

- Strong understanding of RESTful API design, relational and NoSQL databases, and cloud-native architecture patterns

Nice-to-Have:

- Experience with Amazon Bedrock and/or Azure AI Foundry for building and deploying AI/ML solutions on managed cloud platforms

- Experience with Infrastructure as Code (AWS CDK, Terraform, CloudFormation)

- Experience in security infrastructure (IDP, authentication protocols - S) and writing secured applications 

- Security & Identity - understanding of application security, including SSL/TLS certificates, secret management, and identity providers (IDP). Experience implementing authentication and authorization protocols such as SAML, OAuth2, and JWT.

- Foundational SRE knowledge, including monitoring, logging, and observability (e.g., CloudWatch, Prometheus, ELK) to ensure production stability and performance.

- Familiarity with FinOps concepts, cloud cost management, or multi-cloud environments

- Experience building data-driven tools — predictive models, analytics dashboards, or recommendation engines

- Exposure to Azure services alongside AWS

- Experience with containerization and orchestration (Docker, Kubernetes)

- Background in B2B SaaS or enterprise software development