Senior ML Engineer – GenAI, AWS

Role Overview

• Design and implement end-to-end ML solutions from experimentation to production;
• Build scalable ML pipelines and infrastructure;
• Optimize model performance, efficiency, and reliability;
• Write clean, maintainable, production-quality code;
• Conduct rigorous experimentation and model evaluation;
• Troubleshoot and resolve complex technical challenges.
• Mentor junior and mid-level ML engineers;
• Conduct code reviews and provide constructive feedback;
• Share knowledge through documentation, presentations, and workshops;
• Collaborate with cross-functional teams (DevOps, Data Engineering, SAs);
• Contribute to internal ML practice development.
• Stay current with ML research and emerging technologies;
• Propose improvements to existing solutions and processes;
• Contribute to the development of reusable ML accelerators;
• Participate in technical discussions and architectural decisions.

Requirements

• ML Fundamentals: supervised, unsupervised, and reinforcement learning;
• Model Development: feature engineering, model training, evaluation, hyperparameter tuning, and validation;
• ML Frameworks: classical ML libraries, TensorFlow, PyTorch, or similar frameworks;
• Deep Learning: CNNs, RNNs, Transformers.
• LLM Applications: Experience building production LLM-based applications;
• Prompt Engineering: Ability to design effective prompts and chain-of-thought strategies;
• RAG Systems: Experience building retrieval-augmented generation architectures;
• Vector Databases: Familiarity with embedding models and vector search;
• LLM Evaluation: Experience with evaluation metrics and techniques for LLM outputs.
• Python: Advanced proficiency in Python for ML applications;
• Data Manipulation: Expert with pandas, numpy, and data processing libraries;
• SQL: Ability to work with structured data and databases;
• Data Pipelines: Experience building ETL/ELT pipelines
• Big Data: Experience with Spark or similar distributed computing frameworks.
• Model Deployment: Experience deploying ML models to production environments;
• Containerization: Proficiency with Docker and container orchestration;
• CI/CD: Understanding of continuous integration and deployment for ML;
• Monitoring: Experience with model monitoring and observability;
• Experiment Tracking: Familiarity with MLflow, Weights and Biases, or similar tools.
• AWS Services: Strong experience with AWS ML services (SageMaker, Lambda, etc.);
• GCP Expertise: Advanced knowledge of GCP ML and data services;
• Cloud Architecture: Understanding of cloud-native ML architectures;
• Infrastructure as Code: Experience with Terraform, CloudFormation, or similar.

Tech Stack

• AWS
• Cloud
• Docker
• ETL
• Google Cloud Platform
• Numpy
• Pandas
• Python
• PyTorch
• Spark
• SQL
• Tensorflow
• Terraform

Benefits

• Long-term B2B collaboration
• Fully remote setup
• A budget for your medical insurance
• Paid sick leave, vacation, public holidays
• Continuous learning support, including unlimited AWS certification sponsorship.