JOB DESCRIPTION
Come work for a large global financial and insurance products company! This is your chance!!
Start a successful career in a renowned company in the international market! Great opportunity!
- Global insurance and asset management company seeks a responsible, organized, dynamic and team-oriented person.
RESPONSIBILITIES AND ASSIGNMENTS
Role Summary
We are seeking a Senior AI/Integration Engineer to design, build, and operationalize LLM-powered applications, AI copilot experiences, and intelligent API orchestration layers. You will be at the center of our AI engineering practice — integrating foundation models into production systems, building agentic AI workflows, and creating the platform capabilities that enable the broader team to deliver AI-powered solutions at scale.
This is a hands-on, high-impact engineering role. You will work across the full stack of modern AI application development: from prompt engineering and model evaluation to API design, agent orchestration, and production deployment. You should be equally comfortable fine-tuning a retrieval pipeline as you are designing a resilient microservices architecture.
Key Responsibilities
LLM Integration & Application Development
- Design and implement production-grade integrations with foundation models: OpenAI (GPT-4o, GPT-4o-mini, o3), Anthropic (Claude 3.5/4.x), Google (Gemini 3.x), Meta (Llama 3/4), and open-source models via vLLM, Ollama, or Hugging Face
- Build and optimize RAG (Retrieval-Augmented Generation) pipelines — including document ingestion, chunking strategies, embedding generation, vector storage, retrieval ranking, and response synthesis
- Implement advanced prompting techniques: chain-of-thought, few-shot, retrieval-augmented, tool-use, and structured output generation (JSON mode, function calling)
- Design model evaluation frameworks: automated benchmarking, A/B testing, human-in-the-loop evaluation, and regression testing for prompt changes
- Manage model selection, cost optimization, and latency tuning across multiple LLM providers
AI Copilot Development
- Design and build AI copilot experiences — context-aware assistants embedded in applications that augment user workflows with intelligent suggestions, automated actions, and natural language interfaces
- Implement conversational memory management: short-term (session), long-term (user profile), and shared (organizational knowledge)
- Build copilot features including intelligent document summarization, automated report generation, conversational data exploration, and guided workflow assistance
- Design copilot safety layers: content filtering, hallucination detection, confidence scoring, citation generation, and graceful degradation
- Instrument copilots with usage analytics, feedback loops, and continuous improvement mechanisms
Agentic AI & Orchestration
- Architect and build multi-agent AI systems using frameworks like LangGraph, CrewAI, AutoGen, Semantic Kernel, or custom orchestration layers
- Design agent tool-use interfaces: function calling, MCP (Model Context Protocol) servers, and API tool definitions with proper schema validation and error handling
- Implement agent planning and reasoning patterns: ReAct, Plan-and-Execute, Tree-of-Thought, and reflection/self-correction loops
- Build agent guardrails: execution sandboxing, approval workflows for high-stakes actions, resource limits, and audit logging
- Design agent-to-agent communication and coordination for complex multi-step workflows
API Orchestration & Platform Engineering
- Design and build API orchestration layers that compose multiple AI services, internal APIs, and external data sources into cohesive workflows
- Implement API gateway patterns for AI services: rate limiting, request routing, model fallback chains, caching, and usage metering
- Build event-driven architecture using message queues (Kafka, RabbitMQ, SQS) and workflow engines (Temporal, Prefect) for complex AI pipelines
- Design and maintain AI platform SDKs and internal libraries that abstract LLM provider complexity for other engineering teams
- Implement observability for AI systems: prompt/completion logging, token usage tracking, latency monitoring, and cost attribution
Integration Engineering
- Design and implement integrations with enterprise systems: CRM, ERP, ITSM, document management, and communication platforms
- Build robust API connectors with proper authentication (OAuth 2.0, API keys, mTLS), error handling, retry logic, and circuit breakers
- Implement data transformation and mapping layers between AI systems and downstream consumers
- Design webhook and event-driven integration patterns for real-time AI-powered workflows
- Ensure all integrations meet security, compliance, and data governance requirements
REQUIREMENTS AND QUALIFICATIONS
Required Qualifications / Skills
- 6+ years of software engineering experience, with at least 2+ years focused on AI/ML application development and LLM integration
- Production experience integrating and deploying LLM-powered applications using OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, or equivalent platforms
- Deep proficiency in Python (primary) and at least one of: TypeScript/JavaScript, Go, or Java
- Hands-on experience building RAG systems: vector databases (Pinecone, Weaviate, ChromaDB, pgvector), embedding models, retrieval strategies, and evaluation
- Experience with AI orchestration frameworks: LangChain, LangGraph, Semantic Kernel, Haystack, or equivalent
- Strong API design skills: REST, GraphQL, gRPC, WebSockets — with experience building and consuming APIs at scale
- Experience with cloud platforms (AWS, Azure, GCP) and containerized deployments (Docker, Kubernetes)
- Solid understanding of software engineering fundamentals: design patterns, testing strategies, CI/CD, and observability
- Experience with version control (Git), code review practices, and collaborative development workflows
- Fluent English, both written and spoken.
- Proven experience in international projects, including collaboration with global and multicultural teams.
- Strong communication, stakeholder management, and problem-solving skills.
Preferred Qualifications
- Experience building AI copilot or conversational AI products shipped to production users
- Hands-on experience with agentic AI frameworks: LangGraph, CrewAI, AutoGen, Agency Swarm
- Familiarity with MCP (Model Context Protocol), A2A (Agent-to-Agent) Protocol, and emerging AI interoperability standards
- Experience with model fine-tuning, RLHF/DPO, and model distillation techniques
- Background in platform engineering: building internal developer tools, SDKs, and abstraction layers
- Experience with streaming architectures: SSE (Server-Sent Events), WebSockets, and async processing for real-time AI responses
- Knowledge of AI evaluation frameworks: RAGAS, DeepEval, Promptfoo, LangSmith, or custom evaluation pipelines
- Experience in insurance, financial services, or other regulated industries
- Familiarity with AI safety and responsible AI practices: bias detection, hallucination mitigation, and content moderation
Base Requirements
DevOps Experience
- All team members must demonstrate hands-on experience with CI/CD pipelines, containerization (Docker/Kubernetes), cloud platforms, and deployment automation.
Infrastructure as Code
- Proficiency with at least one IaC toolchain (Terraform, Pulumi, CloudFormation/Bicep) is required across all roles — not just DevOps.
Cloud Platforms
- Working knowledge of at least one major cloud provider (AWS, Azure, or GCP).
Version Control & Collaboration
- Git-based workflows, code review practices, and collaborative development are expected of every team member.
Experience Requirements
- Proven delivery experience in international or multi-region projects is required.
- Previous experience mentoring engineers or acting as a technical lead is strongly preferred.
Education
- Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field is preferred.
ADDITIONAL INFORMATION
Modelo de contratação:
Forma de atuação:
SEJAM BEM VINDOS A KEEP SIMPLE 👇🏽
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