Machine Learning Engineer

TekStream Solutions is seeking a Machine Learning Engineer to join the technical team supporting a government client. The role involves developing ML models, implementing and optimizing them, and ensuring data integration and architecture alignment while leveraging cloud services.

Responsibilities:

• 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
• Operationalize models with robust CI/CD workflows
• Deploy models using MLflow, SageMaker, or custom APIs
• Monitor production models for accuracy, drift, and latency; manage retraining schedules
• 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
• Leverage AWS services including S3, EC2, Lambda, SageMaker, and Step Functions
• 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

Requirements:

• 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 systems
• 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
• Possess a Public Trust or ability to obtain a Public Trust
• 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