Design & Execute: Take ownership of the design and implementation of modern AI stack components, including data ingestion for AI/ML workloads and end-to-end model training and serving pipelines.

Scale & Optimize: Build and manage fault-tolerant AI platforms that scale economically. You will balance the maintenance of legacy models with the rapid development of advanced, scalable solutions.

Mentor & Collaborate: Provide technical mentorship to junior engineers and foster a collaborative environment. You will act as a bridge between data science and production engineering.

Drive Technical Excellence: Promote best practices in coding, testing, and MLOps. You thrive in ambiguous conditions by independently identifying opportunities to optimize model pipelines and improve AI workflows.

Cross-Functional Integration: Partner with data scientists, product managers, and software engineers to translate business needs into technical requirements and integrate AI solutions into production applications.

Implement Governance: Enforce model quality standards, integrity, and reliability. You will be responsible for implementing model lineage, fairness, and privacy controls within the automated pipelines.

Monitor & Measure: Build monitoring frameworks to track model performance and system KPIs, ensuring our AI initiatives drive measurable business outcomes.

Experience: Minimum of 4–6 years of professional experience in machine learning engineering, with a proven track record of deploying models into production environments.

Technical Depth: Deep understanding of the modern AI stack, including data ingestion workflows and experience working with curated data warehouses like Snowflake, Databricks, or Redshift.

Cloud Proficiency: At least 3 years of hands-on experience with AWS infrastructure, specifically SageMaker, Spark/AWS Glue, and Infrastructure as Code (IaC) using Terraform.

Orchestration Expert: High proficiency in managing multi-stage workflows using Airflow or similar orchestration systems to automate training and deployment cycles.

MLOps Toolkit: Practical experience with MLflow, Kubeflow, or SageMaker Feature Store to support the end-to-end machine learning lifecycle.

Governance Mindset: Familiarity with model governance practices (lineage, fairness, and privacy) and experience using data cataloging tools for compliance.

Communication: Strong ability to communicate complex technical concepts to non-technical stakeholders and influence project direction.

Industry Context: Experience in FinTech or SaaS environments is a significant advantage.