- Company Name
- Provenir
- Job Title
- Machine Learning Engineer
- Job Description
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**Job Title:** Machine Learning Engineer
**Role Summary:**
Engineer end‑to‑end machine learning and generative AI solutions on AWS. Develop scalable APIs, pipelines, and production‑grade models; manage deployments using containerization and Kubernetes. Drive MLOps practices—including CI/CD, monitoring, and secure deployment—to deliver reliable, cloud‑native SaaS services.
**Expectations:**
- Deep experience with Python (FastAPI, Pytest, unittest) and object‑oriented languages (Java, C#, C++).
- Proven track record of moving ML/LLM prototypes to production in containerized, cloud‑native environments.
- Expertise in AWS services: Bedrock, SageMaker, Lambda, EKS, S3, IAM, CloudWatch, Step Functions, Argo, Kubeflow.
- Hands‑on with Kubernetes (EKS) and Docker workflows.
- Familiarity with generative AI frameworks (LangChain, LlamaIndex), agent orchestration, and RAG.
- Knowledge of vector databases (Pinecone, FAISS, OpenSearch) and embeddings.
- Comfortable building CI/CD pipelines (GitHub Actions) and IaC (Terraform, CloudFormation).
- Strong MLOps mindset: monitoring, logging, scaling, secure deployment.
**Key Responsibilities:**
- Design and implement APIs and reusable ML/GenAI components.
- Build and scale ML pipelines on AWS Bedrock, SageMaker, Step Functions, and Kubernetes.
- Productionise LLMs and generative AI applications with Bedrock.
- Develop and integrate AI agents using LangChain for tool orchestration and RAG.
- Write clean, testable Python code for backend services, SDKs, and ML workflows.
- Package and deploy containerized services on EKS, ensuring scalability, resilience, and observability.
- Maintain CI/CD pipelines, monitoring, and version control practices in a modern MLOps environment.
- Support and maintain cloud‑native SaaS solutions at scale.
**Required Skills:**
- Python (FastAPI, Pytest, unittest)
- Java/C#/C++ (object‑oriented programming)
- Docker & Kubernetes (EKS)
- AWS (Bedrock, SageMaker, Lambda, EKS, S3, IAM, CloudWatch, Step Functions, Argo, Kubeflow)
- Generative AI frameworks (LangChain, LlamaIndex)
- Vector databases & embeddings (Pinecone, FAISS, OpenSearch)
- CI/CD (GitHub Actions)
- IaC (Terraform, CloudFormation)
- MLOps: monitoring, logging, scaling, secure deployment.
**Required Education & Certifications:**
- Bachelor’s degree in Computer Science, Engineering, Data Science, or related field (or equivalent experience).
- Relevant certifications preferred: AWS Certified Solutions Architect, AWS Certified Machine Learning – Specialty.