Key Deliverables

• Deliver end-to-end ML solutions: Architect and implement state-of-the-art models—classification, regression, clustering, reinforcement learning—precisely tuned to solve high-value business problems.
• Engineer data & experimentation pipelines at scale: Build reliable, self-service pipelines for ingesting, cleaning, transforming, and aggregating data, and orchestrate rigorous offline/online experiments (cross-validation, A/B tests) to benchmark accuracy, latency, and resource cost.
• Embed ML seamlessly into products: Partner with data scientists, backend/frontend engineers, and designers to wire models into production services and user experiences, ensuring low-friction integration and measurable product impact.
• Operate, monitor, and evolve models in production: Own the DevOps stack—automated CI/CD, containerization, and cloud deployment—and run real-time monitoring to detect drift, performance degradation, and anomalies, triggering retraining or rollback as needed.
• Uphold engineering excellence & knowledge sharing: Enforce rigorous code quality, version control, testing, and documentation; lead code reviews and mentoring sessions that raise the team’s ML craftsmanship.
• Safeguard data privacy, security, and compliance: Design models and pipelines that meet regulatory requirements, apply robust access controls and encryption, and audit usage to ensure ethical and secure handling of sensitive data.

Qualification & Skills

• Formal grounding in computing & AI: Bachelor’s / Master’s in Computer Science, Data Science, or a related quantitative field.
• Proven production experience: 4+ years shipping, deploying, and maintaining machine-learning models at scale, with a track record of solving complex, real-world problems.
• End-to-end technical toolkit: Python (Pandas, NumPy), ML frameworks (TensorFlow, PyTorch, scikit-learn), databases (SQL & NoSQL), and big-data stacks (Spark, Hadoop).
• MLOps & cloud deployment mastery: Containerization (Docker, Kubernetes), CI/CD pipelines, and monitoring workflows that keep models reliable and reproducible in production.
• Deep applied-ML expertise: Supervised and unsupervised learning, NLP, computer vision, and time-series analysis, plus strong model-evaluation and feature-engineering skills.
• Collaboration & communication strength: Clear communicator and effective team player who can translate business goals into technical solutions and articulate results to diverse stakeholders.