Key Responsibilities
Machine Learning Solutions & Architecture
- Lead end-to-end delivery of machine learning solutions aligned to business priorities.
- Design scalable ML pipelines for feature engineering, training, deployment, monitoring, and optimization.
- Translate business problems into practical AI/ML solutions with measurable outcomes.
- Build reusable architectures and frameworks for enterprise-wide ML adoption.
Intelligent Automation
- Design AI-driven automation solutions using machine learning models and orchestration platforms.
- Improve workflows through predictive analytics, decision automation, and process intelligence.
- Integrate ML solutions with enterprise systems, APIs, BI tools, and RPA/BPA platforms.
- Replace manual or rule-based processes with scalable AI-powered automation.
MLOps & Governance
- Establish CI/CD pipelines for ML models, versioning, monitoring, and retraining processes.
- Define standards for explainability, drift detection, model reliability, and performance.
- Ensure all solutions meet enterprise security, governance, and compliance requirements.
- Partner with platform teams to build secure and scalable AI infrastructure.
Data & Platform Collaboration
- Work with data engineering teams to build pipelines supporting model training and inference.
- Develop feature stores, training datasets, and production inference flows.
- Ensure data quality, lineage, and observability across ML-critical systems.
Leadership & Innovation
- Mentor engineers and data scientists on ML best practices and architecture.
- Influence enterprise AI strategy, roadmap planning, and use case prioritization.
- Evaluate emerging technologies including Generative AI and automation platforms.
- Promote ethical AI, transparency, and responsible model usage.
Required Qualifications
- Bachelor’s degree in Computer Science, Data Science, Engineering, AI, or related field.
- 6+ years of experience in machine learning engineering, AI engineering, data science, or related areas.
- 6+ years of experience delivering production-grade ML solutions.
- Hands-on experience deploying ML models into enterprise systems.
Preferred Qualifications
- Master’s degree in a related technical field.
- 10+ years of experience in AI, ML platforms, or intelligent automation.
- Experience mentoring engineering teams.
- Experience with Generative AI and large language model applications.
- Relevant certifications in AWS, Azure, GCP, Databricks, Snowflake, or MLOps.
Technical Skills
- Strong knowledge of supervised, unsupervised, and reinforcement learning.
- Experience with TensorFlow, PyTorch, Scikit-learn, or similar frameworks.
- Expertise in batch and real-time model deployment.
- Knowledge of model explainability, fairness, and bias mitigation.
- Strong Python and SQL programming skills.
- Experience with AWS, Azure, GCP, Databricks, or Snowflake.
- Understanding of distributed data processing and data engineering fundamentals.
- Strong solution architecture and systems thinking skills.
Leadership Competencies
- Strategic decision-making with customer-first mindset.
- Continuous improvement and innovation focus.
- Strong analytical thinking and problem-solving skills.
- Ability to lead cross-functional teams and influence without authority.
- Commitment to talent development and team growth.