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.Responsabilidades e atribuiçõesRole SummaryWe 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 ResponsibilitiesLLM Integration & Application DevelopmentDesign 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 FaceBuild and optimize RAG (Retrieval-Augmented Generation) pipelines — including document ingestion, chunking strategies, embedding generation, vector storage, retrieval ranking, and response synthesisImplement 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 changesManage model selection, cost optimization, and latency tuning across multiple LLM providersAI Copilot DevelopmentDesign and build AI copilot experiences — context-aware assistants embedded in applications that augment user workflows with intelligent suggestions, automated actions, and natural language interfacesImplement 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 assistanceDesign copilot safety layers: content filtering, hallucination detection, confidence scoring, citation generation, and graceful degradationInstrument copilots with usage analytics, feedback loops, and continuous improvement mechanismsAgentic AI & OrchestrationArchitect and build multi-agent AI systems using frameworks like LangGraph, CrewAI, AutoGen, Semantic Kernel, or custom orchestration layersDesign agent tool-use interfaces: function calling, MCP (Model Context Protocol) servers, and API tool definitions with proper schema validation and error handlingImplement agent planning and reasoning patterns: ReAct, Plan-and-Execute, Tree-of-Thought, and reflection/self-correction loopsBuild agent guardrails: execution sandboxing, approval workflows for high-stakes actions, resource limits, and audit loggingDesign agent-to-agent communication and coordination for complex multi-step workflowsAPI Orchestration & Platform EngineeringDesign and build API orchestration layers that compose multiple AI services, internal APIs, and external data sources into cohesive workflowsImplement API gateway patterns for AI services: rate limiting, request routing, model fallback chains, caching, and usage meteringBuild event-driven architecture using message queues (Kafka, RabbitMQ, SQS) and workflow engines (Temporal, Prefect) for complex AI pipelinesDesign and maintain AI platform SDKs and internal libraries that abstract LLM provider complexity for other engineering teamsImplement observability for AI systems: prompt/completion logging, token usage tracking, latency monitoring, and cost attributionIntegration EngineeringDesign and implement integrations with enterprise systems: CRM, ERP, ITSM, document management, and communication platformsBuild robust API connectors with proper authentication (OAuth 2.0, API keys, mTLS), error handling, retry logic, and circuit breakersImplement data transformation and mapping layers between AI systems and downstream consumersDesign webhook and event-driven integration patterns for real-time AI-powered workflowsEnsure all integrations meet security, compliance, and data governance requirementsRequisitos e qualificaçõesRequired Qualifications / Skills6+ years of software engineering experience, with at least 2+ years focused on AI/ML application development and LLM integrationProduction experience integrating and deploying LLM-powered applications using OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, or equivalent platformsDeep proficiency in Python (primary) and at least one of: TypeScript/JavaScript, Go, or JavaHands-on experience building RAG systems: vector databases (Pinecone, Weaviate, ChromaDB, pgvector), embedding models, retrieval strategies, and evaluationExperience with AI orchestration frameworks: LangChain, LangGraph, Semantic Kernel, Haystack, or equivalentStrong API design skills: REST, GraphQL, gRPC, WebSockets — with experience building and consuming APIs at scaleExperience with cloud platforms (AWS, Azure, GCP) and containerized deployments (Docker, Kubernetes)Solid understanding of software engineering fundamentals: design patterns, testing strategies, CI/CD, and observabilityExperience with version control (Git), code review practices, and collaborative development workflowsFluent 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 QualificationsExperience building AI copilot or conversational AI products shipped to production usersHands-on experience with agentic AI frameworks: LangGraph, CrewAI, AutoGen, Agency SwarmFamiliarity with MCP (Model Context Protocol), A2A (Agent-to-Agent) Protocol, and emerging AI interoperability standardsExperience with model fine-tuning, RLHF/DPO, and model distillation techniquesBackground in platform engineering: building internal developer tools, SDKs, and abstraction layersExperience with streaming architectures: SSE (Server-Sent Events), WebSockets, and async processing for real-time AI responsesKnowledge of AI evaluation frameworks: RAGAS, DeepEval, Promptfoo, LangSmith, or custom evaluation pipelinesExperience in insurance, financial services, or other regulated industriesFamiliarity with AI safety and responsible AI practices: bias detection, hallucination mitigation, and content moderationBase RequirementsDevOps Experience All team members must demonstrate hands-on experience with CI/CD pipelines, containerization (Docker/Kubernetes), cloud platforms, and deployment automation.Infrastructure as CodeProficiency 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 RequirementsProven delivery experience in international or multi-region projects is required.Previous experience mentoring engineers or acting as a technical lead is strongly preferred.EducationBachelor's degree in Computer Science, Information Systems, Engineering, or a related field is preferred.Informações adicionaisModelo de contratação:PJForma de atuação:100% Remota