Role and ResponsibilitiesModel Development
- Collaborate with data scientists and SMEs to develop ML models using curated datasets.
- Conduct experiments, prototypes, and proof-of-concepts to validate model performance.
- Create scalable and reusable training pipelines using Databricks notebooks and MLflow.
Implementation and Optimisation
- LLMs (Large Language Models), RAGs, and AI agent systems for various business applications. Deployment & MLOps
- Operationalize models with robust CI/CD workflows.
- Deploy models usingMLflow, SageMaker, or custom APIs.
- Monitor production models for accuracy, drift, and latency; manage retraining schedules.
Data Integration & Architecture Alignment
- Work closely with Data Engineering to align ML pipelines with the Bronze, Silver, Gold layers of a Medallion Architecture.
- Engineer high-quality features and maintain training/inference pipelines.
Cloud and Platform Engineering
- Leverage AWS services including S3, EC2, Lambda, SageMaker, and Step Functions.
Collaboration & Documentation
- Document ML artifacts, processes, and performance outcomes.
- Contribute to agile project ceremonies and maintain a feedback loop with stakeholders.
- Share knowledge and mentor junior team members.
Required Skills:- 5+ years of experience in ML Engineering or Applied Machine Learning.
- Strong Python skills and hands-on experience with ML libraries (e.g., scikit-learn, XGBoost, PyTorch, TensorFlow).
- Proficient with Databricks, MLflow, and PySpark.
- Solid understanding of model lifecycle and MLOps practices.
- Experience with AWS-based data infrastructure and related DevOps practices.
- Demonstrated ability to productionize models and integrate with business system
- Strong understanding of mathematics and statistics relevant to machine learning and AI.
- Proven experience with machine learning models and algorithms (supervised, unsupervised, deep learning, etc.).
- Solid background in software engineering principles and best practices.
- Hands-on experience with model training frameworks (e.g., TensorFlow, PyTorch, Hugging Face).
- Experience with MLOps tools and workflows, particularly on AWS (SageMaker, Lambda, S3, etc.).
- Practical experience with LLMs, RAGs, and AI agent architectures.
- Proficiency with the Databricks platform for data engineering and ML pipelines.
- Advanced programming skills in Python.
- Excellent communication and teamwork abilities.
Preferred Skills:- Experience building and deploying interactive UIs for AI models using Streamlit, Gradio, or similar frameworks for rapid prototyping and real-time model interactions
- Business acumen and ability to align AI solutions with organizational goals.
- Optimize compute and storage resources for performance and cost-efficiency.
Halvik's pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law.
