Provenir is looking for a Machine Learning Engineer to design, develop, and deploy machine learning models and AI systems that solve real-world problems, turning data into actionable insights and intelligent solutions.
Requirements
- Strong Python development skills, including experience with modern frameworks (FastAPI preferred) and testing practices (pytest, unittest)
- Proven ability to take ML/LLM models from prototype to production in containerized, cloud-native environments
- Hands-on experience with deploying and managing services on Kubernetes (EKS) and Docker-based workflows
- Expertise with AWS services: Bedrock, SageMaker, Lambda, EKS, S3, IAM, CloudWatch
- Familiarity with Generative AI frameworks (LangChain, LlamaIndex) and Agent development (tool integration, RAG workflows)
- Knowledge of vector databases and embeddings (e.g., Pinecone, FAISS, OpenSearch)
- Experience with CI/CD pipelines (GitHub Actions preferred) and Infrastructure as Code (Terraform, CloudFormation)
Responsibilities
- Designing and developing APIs and reusable components for ML and GenAI deployment
- Building and scaling ML/GenAI pipelines on AWS (Bedrock, SageMaker, Step Functions, Argo, Kubeflow on EKS)
- Productionising LLMs, generative AI applications, and predictive models on Amazon Bedrock
- Developing and integrating AI Agents with frameworks like LangChain for orchestration and tool use
- Writing robust, testable, and maintainable Python code for backend services, SDKs, and ML workflows
- Packaging and deploying containerized services on Kubernetes (EKS) with attention to scalability, resilience, and observability
- Working in a modern MLOps environment with CI/CD, monitoring, and version control best practices
Other
- remote role
- ideally suited for candidates based in the U.S. East Coast time zone
- We are only considering candidates who are authorized to work in the United States without the need for current or future visa sponsorship.
- comprehensive health and wellness plans
- paid time off and company holidays, flexible and remote-friendly opportunities, and maternity/paternity leave.
