Senior ML Engineer (GenAI)

- 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.

- 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.