Machine Learning Engineer

We re looking for an ML Engineer who can ship from classical pipelines to LLM-powered features on AWS. You ll design, deploy, and maintain ML systems in production. This is an engineering role first; research experience alone won t be enough. Responsibilities Build end-to-end ML pipelines: data ingestion, training, evaluation, deployment, and monitoring. Design and implement RAG pipelines, prompt engineering systems, and LLM-based features with proper evaluation not vibe-based iteration. Fine-tune open-weight models (LoRA/QLoRA) when API calls aren t the right answer. Deploy and serve models on AWS SageMaker, Bedrock, Lambda, or ECS depending on requirements. Write infrastructure as code (CDK or Terraform); no manual console configuration in production. Monitor deployed models for drift, quality degradation, and cost; own issues through to resolution. Translate ambiguous business problems into concrete ML problem framings. Must-Have Python Engineering-level testable, reviewable code, not just scripts Classical ML Supervised/unsupervised methods; knows when not to use a neural network LLM Fundamentals Genuine understanding of transformers, tokenization, context windows, inference behaviour RAG Has built and evaluated at least one production or near-production RAG system AWS Core S3, IAM, Lambda, EC2, VPC comfortable without a handbook AWS ML SageMaker (Training Jobs + Endpoints) and/or Bedrock Docker Containerising ML workloads for deployment SQL Comfortable writing queries for data extraction and validation Preferred Skills Good to Have Fine-tuning with LoRA/QLoRA (Hugging Face PEFT/TRL) LLM evaluation frameworks RAGAS, DeepEval, LLM-as-judge, or custom Vector databases pgvector, Pinecone, OpenSearch (production, not demos) Agent frameworks LangGraph, LlamaIndex, or custom tool-use implementations Workflow orchestration Step Functions, SageMaker Pipelines, Airflow Infrastructure as Code AWS CDK or Terraform Experiment tracking MLflow or Weights Biases Technology Stack Language: Python ML: Scikit-learn, XGBoost, PyTorch LLM / Models: AWS Bedrock, OpenAI API, Llama / Mistral / Qwen Fine-Tuning: Hugging Face Transformers, PEFT, TRL RAG / Agents: LangChain, LlamaIndex, LangGraph Vector Stores: pgvector, Pinecone, OpenSearch AWS: SageMaker, Bedrock, S3, Lambda, ECS, Step Functions, CDK MLOps: MLflow, WB, Docker, GitHub Actions Data Disclaimer : This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.