Kalo Tech Studio - AI Lead Engineer

Role : AI Lead Engineer Role & Responsibilities : The AI Lead Engineer will be responsible for architecting, developing, and delivering enterprise-scale Artificial Intelligence (AI), Machine Learning (ML), and Generative AI (GenAI) solutions. The role involves leading engineering teams, defining AI architecture, building production-ready LLM applications, implementing MLOps best practices, and driving end-to-end AI solution delivery across cloud environments. Technical Skills & Expertise : Artificial Intelligence & Machine Learning : Machine LearningDeep LearningGenerative AI (GenAI)Large Language Models (LLMs)Natural Language Processing (NLP)Computer Vision (Good to Have)Reinforcement Learning (Preferred) LLMs & AI Frameworks : OpenAIAzure OpenAIClaudeGeminiLlamaMistralHugging Face TransformersLangChainLangGraphLlamaIndexAgentic AI FrameworksRetrieval-Augmented Generation (RAG)Prompt Engineering Programming : PythonSQLREST APIsFastAPIObject-Oriented Programming (OOP) Machine Learning Frameworks : TensorFlowPyTorchScikit-learnXGBoostPandasNumPy MLOps & LLMOps : MLflowKubeflowAirflowModel DeploymentModel MonitoringCI/CD for MLModel VersioningExperiment TrackingLLMOps Best Practices Vector Databases : PineconeFAISSChromaDBWeaviateQdrant Cloud Platforms : Microsoft AzureAmazon Web Services (AWS)Google Cloud Platform (GCP)Azure AI ServicesVertex AIAmazon SageMaker DevOps & Containerization : DockerKubernetesGitCI/CD PipelinesTerraform (Preferred) Architecture & Governance : Enterprise AI ArchitectureAI Solution DesignResponsible AIAI SecurityScalability &Performance OptimizationAI Governance Preferred Candidate Profile : 1. AI Solution Architecture : Architect enterprise-scale AI, ML, and Generative AI solutions aligned with business objectives.Design scalable AI platforms, LLM-powered applications, and intelligent automation solutions.Define enterprise AI architecture standards, reusable frameworks, and engineering best practices.Evaluate trade-offs across scalability, performance, security, and cost. 2. Generative AI & LLM Development : Lead the design and implementation of LLM-powered applications using OpenAI, Azure OpenAI, Claude, Gemini, Llama, and other foundation models.Build Retrieval-Augmented Generation (RAG) pipelines, AI agents, and conversational AI systems.Develop prompt engineering strategies and AI workflow orchestration.Design and optimize vector database architectures and embedding pipelines. 3. Machine Learning & MLOps : Lead the end-to-end machine learning lifecycle, including model development, deployment, monitoring, and optimization.Implement MLOps and LLMOps best practices using MLflow, Kubeflow, Airflow, and cloud-native AI services.Ensure model reliability, scalability, and continuous improvement through automated pipelines.Drive experimentation, model versioning, and performance monitoring. 4. Cloud & Platform Engineering : Design and deploy AI solutions across Azure, AWS, and GCP.Build cloud-native AI platforms using containerized deployments with Docker and Kubernetes.Optimize AI infrastructure for performance, resilience, and operational efficiency.Implement CI/CD pipelines and Infrastructure as Code for AI workloads. 5. Leadership & Team Management : Lead and mentor AI/ML engineers, data scientists, and software engineers.Drive technical decision-making, architecture reviews, and code quality.Collaborate with Product Managers, Architects, Data Engineers, and business stakeholders to deliver AI initiatives.Foster a culture of innovation, continuous learning, and engineering excellence. 6. Innovation & Research : Stay current with advancement .