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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and delivering production-grade Generative AI and LLM-based solutions, with a strong foundation in Python development and software engineering principles. Proficient in evaluating AI frameworks, orchestration patterns, and cloud-native practices to ensure scalable and reliable AI applications.
Highest-signal resume keywords
Python DevelopmentGenerative AI SolutionsAI Frameworks (LangGraph, LangChain, etc.)Cloud-Native PracticesAI Evaluation and Observability
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
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Hard Skills
Software Design PrinciplesAPIs and Asynchronous WorkflowsEmbeddings and Vector SearchMulti-Agent SystemsDockerCI/CDAutomated TestingVersion ControlRAG and Agentic AI PatternsTechnical Trade-Off Assessment
Soft Skills
CollaborationTechnical GuidanceProblem-SolvingCommunication
Tools & Technologies
OpenAI Agents SDKDatadog LLM ObservabilityLangSmithOpenTelemetryGitHub
Industry Keywords
Human-in-the-LoopLong-Running WorkflowsGuardrailsObservability PracticesEnterprise Knowledge Retrieval
Tech Stack
Tools & technologiesAWSAzureCloudDockerPythonSDLC
About the role
Key responsibilities & impact- Own AI solutions from technical discovery through production and continuous improvement
- Design and evolve Generative AI, RAG, and agentic applications
- Define technical approaches considering quality, scalability, security, cost, maintainability, and operational impact
- Design agents and workflows that interact with tools, APIs, databases, messaging systems, and enterprise platforms
- Define orchestration patterns such as Human-in-the-Loop, long-running workflows, retries, fallback strategies, and deterministic controls
- Establish evaluation, observability, guardrails, and reliability practices for AI applications
- Guide model, retrieval, framework, and infrastructure choices based on technical trade-offs
- Troubleshoot complex production issues involving models, prompts, retrieval, tools, integrations, and infrastructure
- Collaborate with architects, engineers, product teams, and business stakeholders, providing technical guidance when needed
- Support other engineers and contribute to engineering standards and best practices for AI solutions
Requirements
What you’ll need- Strong Python development skills and solid software engineering foundations
- Proven ability to design and deliver production-grade Generative AI and LLM-based solutions
- Deep knowledge of software design principles, including modularity, separation of concerns, testability, maintainability, resiliency, and clean interfaces
- Ability to design services, APIs, asynchronous workflows, and distributed components for production environments
- Hands-on knowledge of RAG and agentic AI patterns, including tool use, function calling, orchestration, Human-in-the-Loop, and guardrails
- Strong understanding of embeddings, vector search, retrieval strategies, chunking, reranking, and enterprise knowledge retrieval
- Proficiency with AI frameworks or SDKs such as LangGraph, LangChain, Semantic Kernel, LlamaIndex, OpenAI Agents SDK, Strands, or similar
- Practical knowledge of multiple LLM providers such as OpenAI, Anthropic Claude, Google Gemini, Azure OpenAI, AWS Bedrock, or similar
- Ability to assess trade-offs between models, providers, orchestration approaches, retrieval strategies, and infrastructure options
- Strong knowledge of AI evaluation and observability, including quality, tracing, latency, token usage, failures, and cost
- Good understanding of cloud-native practices such as scalability, resiliency, secrets management, configuration management, observability, and access control
- Strong working knowledge of Docker, CI/CD, automated testing, and version control
- Fluent English for technical discussions with international stakeholders
- Knowledge of multi-agent systems and long-running agent workflows
- Background in Agentic SDLC, coding agents, GitHub, or developer tooling integrations
- Familiarity with knowledge graphs, GraphRAG, or hybrid retrieval architectures
- Hands-on knowledge of Datadog LLM Observability, LangSmith, OpenTelemetry, or similar platforms
Benefits
Comp & perks- Health and dental insurance
- Meal and food allowance
- Childcare assistance
- Extended paternity leave
- Partnership with gyms and health and wellness professionals via Wellhub (Gympass) TotalPass
- Profit Sharing and Results Participation (PLR)
- Life insurance
- Continuous learning platform (CI&T University)
- Discount club
- Free online platform dedicated to physical, mental, and overall well-being
- Pregnancy and responsible parenting course
- Partnerships with online learning platforms
- Language learning platform
- Health and Well-being team, inclusion specialists, and affinity groups
- Support and accommodations for people with disabilities during the selection process