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Responsibilities
Responsibilities:Technical Delivery (60%)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.Collaboration and Contribution (25%);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.Innovation and Growth (15%)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:Machine Learning CoreML 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.LLMs and Generative AILLM 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.Data and ProgrammingPython: 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.MLOps and ProductionModel 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.Cloud and InfrastructureAWS 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.Will be a plus:Practical experience with cloud platforms (AWS stack is preferred, e.g. Amazon SageMaker, ECR, EMR, S3, AWS Lambda);Practical experience with deep learning models;Experience with taxonomies or ontologies;Practical experience with machine learning pipelines to orchestrate complicated workflows;Practical experience with Spark/Dask, Great Expectations.What We Offer: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.Interview stages:Recruitment Interview;Tech interview;HR Interview;HM Interview.
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