About the Role<\/p>\n
\tOur client is seeking a Full-Stack AI Engineer to design, build, and deploy AI-powered applications that bridge modern software engineering with applied machine learning. This role focuses on taking AI solutions from prototype to production \u2014 ensuring systems are scalable, reliable, secure, and optimized for real-world business impact.
\n\tThe ideal candidate combines strong full-stack engineering skills with hands-on experience integrating LLMs, machine learning models, vector databases, and AI workflows into production environments. You will work closely with product, engineering, and data teams to build intelligent applications that improve automation, user experience, and operational efficiency.
\n\tThis is a highly technical, execution-focused role for someone comfortable owning AI systems end-to-end \u2014 from infrastructure and APIs to front-end experiences and deployment pipelines.<\/p>\n
Responsibilities
\nAI Model Integration & Deployment<\/p>\n
\tDeploy and integrate pre-trained and fine-tuned ML\/LLM models using platforms such as OpenAI, Hugging Face, TensorFlow, and PyTorch
\n\tBuild scalable inference APIs using FastAPI, Flask, Node.js, or similar frameworks
\n\tImplement vector search and retrieval systems using Pinecone, Weaviate, FAISS, or ChromaDB
\n\tDesign and optimize Retrieval-Augmented Generation (RAG) pipelines for AI-powered applications
\n\tMonitor model accuracy, latency, and operational performance in production environments<\/p>\n
Data Engineering & AI Pipelines<\/p>\n
\tBuild ETL pipelines for ingesting, cleaning, transforming, and processing structured and unstructured datasets
\n\tAutomate data preprocessing, labeling, validation, and versioning workflows
\n\tManage datasets and pipelines using Airflow, Prefect, Dagster, or similar orchestration tools
\n\tStore and manage datasets in cloud data warehouses such as BigQuery, Snowflake, or Redshift
\n\tOptimize pipelines for scalability, reliability, and cost efficiency<\/p>\n
Full-Stack Application Development<\/p>\n
\tBuild front-end interfaces in React, Next.js, or Vue for AI-powered features such as chatbots, dashboards, search, and analytics tools
\n\tDevelop scalable back-end services and microservices that connect AI models to business logic
\n\tEnsure applications are responsive, secure, intuitive, and production-ready
\n\tDesign APIs and services that support high concurrency and scalable AI workloads<\/p>\n
Infrastructure, DevOps & Deployment<\/p>\n
\tContainerize services using Docker and deploy workloads to Kubernetes environments
\n\tBuild and maintain CI\/CD pipelines for application and model deployments
\n\tMonitor infrastructure health, inference latency, system uptime, and operational costs
\n\tImplement observability and monitoring using MLflow, Weights & Biases, Datadog, Prometheus, or custom dashboards
\n\tOptimize AI inference performance and infrastructure costs across environments<\/p>\n
Security & Compliance<\/p>\n
\tEnsure AI systems comply with GDPR, HIPAA, SOC 2, and other applicable data privacy standards
\n\tImplement secure authentication, access controls, rate limiting, and API security best practices
\n\tMaintain secure handling of sensitive user and business data<\/p>\n
Collaboration & Product Development<\/p>\n
\tWork closely with data scientists to productionize experimental models and prototypes
\n\tPartner with product and engineering teams to scope and prioritize AI-driven features
\n\tContribute to architecture discussions and technical planning
\n\tDocument workflows, APIs, infrastructure, and AI systems for maintainability and reproducibility<\/p>\n
What Makes You a Perfect Fit<\/p>\n
\tStrong engineer with hands-on experience across both software development and applied AI\/ML
\n\tComfortable moving quickly from experimentation to production deployment
\n\tAnalytical problem solver who balances scalability, latency, usability, and cost
\n\tCurious and adaptable, constantly exploring emerging AI frameworks, tools, and workflows
\n\tOwnership-driven with the ability to independently execute complex technical initiatives
\n\tStrong communicator capable of collaborating across technical and non-technical teams<\/p>\n
Required Experience & Skills<\/p>\n
\t3+ years of software engineering experience with exposure to AI\/ML systems
\n\tStrong proficiency in Python and JavaScript\/TypeScript
\n\tHands-on experience with AI\/ML frameworks such as PyTorch, TensorFlow, Hugging Face, or OpenAI APIs
\n\tExperience building scalable APIs and back-end systems
\n\tFront-end development experience using React, Next.js, Vue, or similar frameworks
\n\tExperience deploying machine learning models into production systems
\n\tStrong SQL skills and experience with cloud data warehouses
\n\tFamiliarity with Docker, Kubernetes, and CI\/CD workflows
\n\tExperience integrating APIs, vector databases, and AI inference services<\/p>\n
Ideal Experience & Skills<\/p>\n
\tExperience building and scaling AI-powered SaaS applications
\n\tHands-on experience with embeddings, fine-tuning, and RAG pipelines
\n\tFamiliarity with MLOps platforms such as MLflow, Kubeflow, Vertex AI, or SageMaker
\n\tExperience with serverless architectures and microservices
\n\tKnowledge of prompt engineering and AI workflow optimization
\n\tExperience optimizing inference latency and AI infrastructure costs
\n\tFamiliarity with monitoring model drift, evaluation metrics, and AI observability practices<\/p>\n
What Does a Typical Day Look Like?<\/p>\n
A Full-Stack AI Engineer\u2019s day revolves around building and optimizing production-grade AI systems. You will:<\/p>\n
\tDevelop and refine APIs that expose AI and LLM functionality
\n\tBuild front-end interfaces that surface AI-powered workflows to end users
\n\tMaintain and optimize ETL pipelines for AI model training and inference
\n\tDeploy updates through CI\/CD pipelines and monitor production performance
\n\tTroubleshoot latency, scaling, or infrastructure bottlenecks
\n\tCollaborate with product and data teams to prioritize impactful AI features
\n\tDocument systems and workflows to ensure scalability and maintainability<\/p>\n
In essence: you are responsible for turning AI capabilities into reliable, scalable, and user-friendly production applications.<\/p>\n
Key Metrics for Success (KPIs)<\/p>\n
\tSuccessful deployment of AI-powered features on schedule
\n\tApplication uptime \u2265 99.9%
\n\tInference latency maintained below target thresholds
\n\tReliability and scalability of AI systems in production
\n\tReduction in manual workflows through automation and AI integration
\n\tStable model performance and monitoring accuracy over time
\n\tPositive adoption and usage of AI-driven features by end users
\n\tInfrastructure and inference cost optimization improvements<\/p>\n
go to method of application \u00bb<\/p>\n
Apply via company website ( ) or<\/p>\n
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