Role and Responsibilities
Model 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.
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