This is a permanent role with a valued Big 4 client of People Impact

We are hiring an experienced Machine Learning professional for a permanent position with a leading Big 4 client through People Impact. The role focuses on building, deploying, and maintaining scalable machine learning solutions in production environments. You will work across the complete ML lifecycle, from data engineering and model development to deployment, monitoring, and continuous optimization.

This is a hands-on technical role requiring strong engineering capability, production-grade ML experience, and the ability to collaborate with business and technical stakeholders to deliver impactful AI-driven solutions.

Key Responsibilities

• Design, build, and deploy end-to-end machine learning pipelines in production

• Develop and optimize ML models using supervised and unsupervised learning techniques

• Implement feature engineering, hyperparameter tuning, and model evaluation strategies

• Build and maintain scalable ML systems with strong focus on performance and reliability

• Work on MLOps practices including CI/CD pipelines, model monitoring, and drift detection

• Collaborate with data engineering teams to design robust data pipelines (batch and streaming)

• Ensure model governance including bias detection, explainability, and reproducibility

• Translate business requirements into scalable and production-ready ML solutions

• Work closely with cross-functional stakeholders in a global delivery environment

• Mentor junior team members and contribute to engineering best practices

Technical Skills Required

• 8+ years of experience in Data Science / Machine Learning with production deployment experience

• Strong hands-on expertise in Python and ML frameworks such as scikit-learn, pandas, NumPy, TensorFlow, and PyTorch

• Solid experience in building and deploying ML models end-to-end

• Strong understanding of supervised and unsupervised learning techniques

• Experience with MLOps tools such as MLflow, CI/CD pipelines, model monitoring, and drift detection

• Exposure to cloud platforms such as Azure ML, Databricks, AWS, or GCP

• Strong knowledge of Docker, Kubernetes, and GitHub Actions

• Strong data engineering skills including SQL, APIs, and batch/streaming data processing

Soft Skills

• Strong stakeholder management and communication skills

• Ability to translate business problems into technical ML solutions

• Experience mentoring engineers or data scientists

• Strong ownership and delivery mindset in enterprise environments