{"id":1716953,"url":"https://alion.io/job/cit-senior-ai-engineer","title":"Senior AI Engineer","company":{"id":957,"name":"CI&T","domain":"ciandt.com","url":"https://alion.io/company/ciandt","size_band":"5000+","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"A","score":90,"open_postings":12,"ghost_share":0,"stale_share":0.583,"repost_share":0,"time_to_fill_p50_days":18,"computed_at":"2026-10-06T05:45:30Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":["BR"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":54000,"max_usd":135000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":1757},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Anthropic","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"AWS Strands Agents","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"Function Calling","optional":false},{"name":"Gemini","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"OpenAI","optional":false},{"name":"OpenAI Agents SDK","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Reranking","optional":false},{"name":"Semantic Kernel","optional":false},{"name":"Tool Use","optional":false},{"name":"Datadog","optional":true},{"name":"GitHub","optional":true},{"name":"GraphRAG","optional":true},{"name":"Knowledge Graph","optional":true},{"name":"LangSmith","optional":true},{"name":"Multi-Agent Systems","optional":true},{"name":"OpenTelemetry","optional":true}],"status":"live","first_seen_at":"2026-10-02T14:30:42Z","employer_posted_date":"2026-10-02","last_verified_at":"2026-10-06T18:42:48Z","board_verified":true,"closed_at":null,"days_open":4,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":4},"description":"We are looking for a Senior AI Engineer to join a delivery team building Generative AI and Agentic AI solutions for a global enterprise client.\nYou will help shape and deliver AI solutions from discovery to production, combining strong software engineering foundations with modern AI capabilities. The role involves hands-on work with RAG, AI agents, enterprise integrations, intelligent workflows, evaluation, observability, and cloud environments.\nWhat you will do:\n- Own AI solutions from technical discovery through production and continuous improvement.\n- Design and evolve Generative AI, RAG, and agentic applications.\n- Define technical approaches considering quality, scalability, security, cost, maintainability, and operational impact.\n- Design agents and workflows that interact with tools, APIs, databases, messaging systems, and enterprise platforms.\n- Define orchestration patterns such as Human-in-the-Loop, long-running workflows, retries, fallback strategies, and deterministic controls.\n- Establish evaluation, observability, guardrails, and reliability practices for AI applications.\n- Guide model, retrieval, framework, and infrastructure choices based on technical trade-offs.\n- Troubleshoot complex production issues involving models, prompts, retrieval, tools, integrations, and infrastructure.\n- Collaborate with architects, engineers, product teams, and business stakeholders, providing technical guidance when needed.\n- Support other engineers and contribute to engineering standards and best practices for AI solutions.\nMust-have:\n- Strong Python development skills and solid software engineering foundations.\n- Proven ability to design and deliver production-grade Generative AI and LLM-based solutions.\n- Deep knowledge of software design principles, including modularity, separation of concerns, testability, maintainability, resiliency, and clean interfaces.\n- Ability to design services, APIs, asynchronous workflows, and distributed components for production environments.\n- Hands-on knowledge of RAG and agentic AI patterns, including tool use, function calling, orchestration, Human-in-the-Loop, and guardrails.\n- Strong understanding of embeddings, vector search, retrieval strategies, chunking, reranking, and enterprise knowledge retrieval.\n- Proficiency with AI frameworks or SDKs such as LangGraph, LangChain, Semantic Kernel, LlamaIndex, OpenAI Agents SDK, Strands, or similar.\n- Practical knowledge of multiple LLM providers such as OpenAI, Anthropic Claude, Google Gemini, Azure OpenAI, AWS Bedrock, or similar.\n- Ability to assess trade-offs between models, providers, orchestration approaches, retrieval strategies, and infrastructure options.\n- Strong knowledge of AI evaluation and observability, including quality, tracing, latency, token usage, failures, and cost.\n- Good understanding of cloud-native practices such as scalability, resiliency, secrets management, configuration management, observability, and access control.\n- Strong working knowledge of Docker, CI/CD, automated testing, and version control.\n- Fluent English for technical discussions with international stakeholders.\nNice-to-have:\n- Knowledge of multi-agent systems and long-running agent workflows.\n- Background in Agentic SDLC, coding 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