Machine Learning Engineer

• 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.

• 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

• 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

• 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.