Senior ML Engineer

Core Responsibilities:

• 1. 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


• 2. 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


• 3. 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:

• 1. Machine Learning Core
- 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
• 2. LLMs and Generative AI
- 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
• 3. Data and Programming
- 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
• 4. MLOps and Production
- 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
• 5. Cloud and Infrastructure
- AWS Services: Strong experience with AWS ML services (SageMaker, Lambda, etc.)
- 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.