Senior AI/Integration Engineer (4619)

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.

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

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.

Modelo de contratação:

• PJ

Forma de atuação:

• 100% Remota

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