Pessoa Engenheira de IA Sênior at Jobgether

, , Brazil -

Full Time

Start Date

Immediate

Expiry Date

02 Oct, 26

Salary

0.0

Posted On

04 Jul, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Generative AI, Large Language Models, RAG Architectures, Python, FastAPI, LangChain, LlamaIndex, Vector Databases, Prompt Engineering, Backend Engineering, Microservices, AI Safety, Autonomous Agents, Semantic Search, API Development, System Integration

Industry

Internet Marketplace Platforms

Description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Pessoa Engenheira de IA Sênior based in Brazil. This is a highly technical and hands-on role focused on building and scaling production-grade AI systems powered by large language models. You will be responsible for designing intelligent applications that go beyond experimentation and operate reliably in real-world enterprise environments. The role involves working at the intersection of LLM orchestration, RAG architectures, agentic systems, and backend engineering to deliver end-to-end AI solutions. You will design and optimize data pipelines, context retrieval strategies, and evaluation mechanisms to ensure accuracy, performance, and safety. A key part of your mission will be transforming business needs into robust AI-powered services integrated with APIs, tools, and corporate data systems. This is a fast-paced environment where experimentation meets production engineering, requiring strong technical depth and product thinking. You will collaborate closely with multidisciplinary teams to build scalable, reusable, and high-impact AI components. \n Accountabilities: Design, develop, and maintain applications powered by LLMs, RAG pipelines, autonomous agents, copilots, and intelligent automation systems. Build orchestration flows connecting models, APIs, tools, vector databases, and enterprise data sources. Develop and optimize RAG pipelines, including embeddings, chunking strategies, retrieval, reranking, and response validation. Implement guardrails, fallback mechanisms, evaluation frameworks, and hallucination reduction techniques. Develop APIs and backend services to expose AI capabilities for internal and external consumption. Apply prompt engineering, context engineering, and tool-calling strategies to improve model performance and reliability. Define and monitor metrics related to latency, cost, quality, safety, and system effectiveness. Investigate model failures and propose architectural or data-driven improvements. Collaborate with Data, ML, Platform, Product, and Engineering teams to deliver integrated AI solutions. Ensure security, privacy, traceability, and responsible AI usage across all implementations. Requirements: Strong hands-on experience building production solutions with Generative AI and LLM-based systems. Deep understanding of LLMs, embeddings, RAG architectures, vector databases, and semantic search. Advanced Python skills and experience building APIs, preferably with FastAPI or similar frameworks. Experience with AI orchestration frameworks such as LangChain, LlamaIndex, CrewAI, Semantic Kernel, or LiteLLM. Solid software engineering background including microservices, APIs, relational databases, and system integration. Ability to evaluate model outputs, diagnose issues, and implement technical improvements. Strong awareness of AI safety, privacy, security, and responsible development practices. Experience with autonomous agents, multi-agent systems, or agentic workflows (differential). Familiarity with tool use, function calling, MCP, and external system integrations. Experience with vector databases such as pgvector, Pinecone, Weaviate, Qdrant, or FAISS. Knowledge of LLM evaluation techniques, observability tools, and testing datasets (differential). Experience with open-source models, model routing, and fallback strategies (differential). Understanding of LGPD, data masking, auditing, or regulated environments (differential). Benefits: Cooperative contract model with competitive compensation aligned to market standards Fully remote (Anywhere Office) work model BYOD support for equipment purchase or use of personal machine Home office allowance via flexible benefits card Wellness benefits including TotalPass, WellHub access, and health platforms Additional benefits such as DIT coverage, birthday day off, and discount club access Continuous learning opportunities, training, and certifications Exposure to cutting-edge AI, hyperautomation, and enterprise-scale solutions Collaborative and innovation-driven culture focused on growth and autonomy. \n How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best! Why Apply Through Jobgether? Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time. #LI-CL1

Responsibilities

Design and scale production-grade AI systems using LLMs, RAG pipelines, and agentic workflows. Develop backend services and APIs to integrate AI capabilities into enterprise corporate data systems.