Back
Strong employer brand plus attractive ML architect title but senior specialization limits applicant density.
High specialization in agentic ML, model engineering and platform integration reduces cross-industry transferability.
Numerous mandatory specialized technologies, architecture ownership and security/Responsible AI expectations increase filtering rigidity.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Architect and deliver end-to-end AI/ML solutions including model development, deployment, and integration using Python and relevant frameworks.
Design and manage scalable data pipelines and real-time processing systems with technologies such as Apache Spark, Kafka, PostgreSQL, and data engineering platforms (Airflow, DataBricks, RabbitMQ).
Lead and mentor a technical team; oversee quality, compliance, and operational efficiency while enabling reusable agentic AI components and DevSecOps practices.
Strong Python engineering skills with experience in agent development kits (e.g., Google ADK, LangGraph, OpenAI Agents SDK).
Expert-level proficiency in AI/ML model development using Python, TensorFlow, PyTorch, scikit-learn, and related tools (XGBoost, LightGBM, Spark MLlib).
Experience with big data technologies and relational databases, including Apache Spark, Kafka, Airflow, DataBricks, RabbitMQ, PostgreSQL, and MySQL.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in architecting complex, scalable AI/ML systems integrating multiple technologies and frameworks in production environments.
Able to translate ambiguous business challenges into measurable AI/ML solutions with operational impact and strong efficiency focus.
Skilled leader comfortable setting technical direction and mentoring a focused team of engineers in advanced AI/ML and agentic AI capabilities.