AI & ML Services – Senior Machine Learning Engineer

Role Summary Design, develop, deploy, and maintain scalable Machine Learning and Generative AI solutions that address complex business challenges. The role is responsible for building end-to-end ML systems, Agentic AI architectures, Retrieval-Augmented Generation (RAG) platforms, model serving APIs, and AI-enabled applications while partnering with business stakeholders, data scientists, product owners, and engineering teams. The Senior Machine Learning Engineer will drive MLOps best practices, model governance, cloud-native deployments, and production-grade AI solutions leveraging modern cloud and AI ecosystems. Responsibilities Machine Learning Engineering Design, build, test, deploy, and maintain Machine Learning models and AI-powered applications. Develop scalable feature engineering, training, validation, and inference pipelines. Build reusable ML frameworks and services for model deployment and monitoring. Translate business requirements into measurable ML use cases, success metrics, and technical solutions. Collaborate with Data Scientists to operationalize predictive and generative AI models. Optimize model performance, scalability, reliability, and cost efficiency. Generative AI & Agentic AI Design and implement Agentic AI solutions using multi-agent architectures. Build AI agents using LangChain, LangGraph, CrewAI, AutoGen, or similar frameworks. Develop advanced prompt engineering strategies and orchestration workflows. Implement LLM-powered applications leveraging foundation models such as Gemini, GPT, Claude, or open-source alternatives. Design and deploy conversational AI solutions and enterprise copilots. Retrieval-Augmented Generation (RAG) Design and implement end-to-end RAG architectures. Develop vector search solutions using embeddings and semantic search techniques. Integrate vector databases and enterprise knowledge repositories. Implement hybrid search, semantic retrieval, document chunking, ranking, and retrieval optimization. Evaluate and improve retrieval accuracy, grounding, and response quality. Model Optimization & AI Evaluation Fine-tune foundation models and domain-specific language models. Implement model evaluation frameworks and benchmarking methodologies. Define AI quality metrics including: Accuracy Relevance Groundedness Hallucination Detection Toxicity & Safety Latency Cost Efficiency Conduct A/B testing and model comparison using industry-standard leaderboards and evaluation frameworks. Cloud AI & MLOps Deploy and manage AI/ML solutions in GCP, Azure, AWS, or hybrid cloud environments. Implement CI/CD pipelines for Machine Learning and Generative AI workloads. Build automated model retraining and deployment workflows. Manage model lifecycle, versioning, governance, and auditability. Support production environments and ensure high availability and observability. Required Technical Skills Artificial Intelligence & Machine Learning Machine Learning Deep Learning Natural Language Processing (NLP) Transformer Architectures Supervised & Unsupervised Learning Model Training & Optimization Feature Engineering Model Evaluation Fine-Tuning Techniques Generative AI Large Language Models (LLMs) Agentic AI Multi-Agent Architectures Prompt Engineering RAG Architectures Embeddings Vector Databases AI Evaluation Frameworks Programming Languages Python (Advanced) SQL (Advanced) Machine Learning Frameworks TensorFlow PyTorch JAX Scikit-Learn AI Frameworks LangChain LangGraph LlamaIndex CrewAI (Preferred) AutoGen (Preferred) Cloud Platforms Google Cloud Platform (Preferred) Microsoft Azure Amazon Web Services Preferred Knowledge Areas Vertex AI Ecosystem Vertex AI Pipelines Vertex AI Workbench Vertex AI Model Registry Vertex AI Feature Store Vertex AI Studio Gemini Models Data Architecture BigQuery BigQuery ML Cloud Storage Enterprise Data Warehousing Data Lake Architectures Data Engineering Apache Beam Google Cloud Dataflow Dataproc Spark Streaming Data Processing Batch Processing MLOps & Automation Kubeflow Cloud Composer (Airflow) CI/CD for ML Infrastructure as Code Model Governance Model Lifecycle Management Containerization & Deployment Docker Kubernetes GKE Cloud Run REST APIs Model Serving Platforms Monitoring & Observability Model Drift Detection Data Drift Monitoring Feature Drift Analysis Latency Monitoring Performance Monitoring Responsible AI Practices Nice to Have Experience with Gemini ecosystem and Vertex AI Studio. Experience building enterprise AI copilots and agent-based workflows. Knowledge of Responsible AI, AI Governance, and AI Security. Experience with reinforcement learning and advanced AI evaluation methodologies. Contributions to AI/ML open-source projects. Exposure to multimodal AI solutions (text, image, speech, video). Experience Level Senior (7+ years of Data Science / Machine Learning experience) Education Bachelor's or Master's degree in: Computer Science Artificial Intelligence Data Science Machine Learning Software Engineering Related Technical Field Keywords for LinkedIn Search Mandatory Machine Learning Python LLM RAG LangChain LangGraph Vertex AI GCP TensorFlow OR PyTorch NLP Agentic AI Highly Preferred Gemini BigQuery Kubeflow Dataflow Apache Beam MLOps Feature Store Fine Tuning Vector Database Multi-Agent Systems … mais Requisitos adicionados pelo anunciante da vaga • Mais de 4 anos de experiência profissional com Large Language Models (LLM) • Mais de 3 anos de experiência profissional com Generative AI • Mais de 4 anos de experiência profissional com Databases Pesquise vagas mais rápido com Premium Acesse informações sobre empresas, como prioridades estratégicas, tendências de número de funcionários e muito mais Jonatas e outros milhões de usuários têm contas Premium Reative o Premium Aproveite o suporte 24 horas e cancele quando quiser.