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TD Newton
About the role
Lead Machine Learning Engineer
Responsibilities
- Lead and own the end‑to‑end production lifecycle of ML and LLM models (must have), ensuring models are deployable, scalable, observable, and maintainable.
- Define and enforce ML engineering and MLOps standards across teams (must have).
- Design and maintain CI/CD pipelines for ML workloads (must have).
- Act as technical lead and mentor for ML engineers and contributors (must have).
- Partner with Data Scientists to industrialize research into production systems (must have).
- Collaborate with Platform, Cloud, and Data Engineering teams on infrastructure and runtime alignment (must have).
- Own model monitoring, drift detection, testing, rollback, and incident analysis (must have).
- Evaluate and introduce new ML, GenAI, and MLOps tools with a pragmatic, enterprise mindset (good to have).
- Contribute to ML governance, reproducibility, and responsible AI practices (good to have).
Key Skills & Expertise
- Cloud & DevOps : Azure (must have), CI/CD using Jenkins, GitHub Actions, ArgoCD (must have)
- Container & Orchestration : Docker, Kubernetes (must have)
- Workflow Orchestration : Airflow (must have)
- Programming : Production‑grade Python (must have)
- ML Engineering & GenAI : LLM integration, prompt engineering, model packaging and lifecycle management (must have)
- Testing & Quality : Pytest, integration and system testing for ML systems (must have)
- Data : SQL, relational databases, basic reporting and dashboards (must have)
- ML Platforms : MLflow, Databricks (good to have)
- LLM Frameworks : LangChain, LangGraph, agent‑based patterns (good to have)
- Data Science Awareness : ML algorithms, feature engineering, evaluation metrics, bias/leakage awareness (awareness required)
- Specialized Use Cases : OCR and document processing pipelines (good to have)
- Frontend / Visualization : Streamlit, widgets, lightweight UI layers (good to have)
- Mindset : Awareness of emerging technologies and new tooling (good to have)
Profile
Lead Machine Learning Engineer
Responsibilities
- Lead and own the end‑to‑end production lifecycle of ML and LLM models (must have), ensuring models are deployable, scalable, observable, and maintainable.
- Define and enforce ML engineering and MLOps standards across teams (must have).
- Design and maintain CI/CD pipelines for ML workloads (must have).
- Act as technical lead and mentor for ML engineers and contributors (must have).
- Partner with Data Scientists to industrialize research into production systems (must have).
- Collaborate with Platform, Cloud, and Data Engineering teams on infrastructure and runtime alignment (must have).
- Own model monitoring, drift detection, testing, rollback, and incident analysis (must have).
- Evaluate and introduce new ML, GenAI, and MLOps tools with a pragmatic, enterprise mindset (good to have).
- Contribute to ML governance, reproducibility, and responsible AI practices (good to have).
Key Skills & Expertise
- Cloud & DevOps : Azure (must have), CI/CD using Jenkins, GitHub Actions, ArgoCD (must have)
- Container & Orchestration : Docker, Kubernetes (must have)
- Workflow Orchestration : Airflow (must have)
- Programming : Production‑grade Python (must have)
- ML Engineering & GenAI : LLM integration, prompt engineering, model packaging and lifecycle management (must have)
- Testing & Quality : Pytest, integration and system testing for ML systems (must have)
- Data : SQL, relational databases, basic reporting and dashboards (must have)
- ML Platforms : MLflow, Databricks (good to have)
- LLM Frameworks : LangChain, LangGraph, agent‑based patterns (good to have)
- Data Science Awareness : ML algorithms, feature engineering, evaluation metrics, bias/leakage awareness (awareness required)
- Specialized Use Cases : OCR and document processing pipelines (good to have)
- Frontend / Visualization : Streamlit, widgets, lightweight UI layers (good to have)
- Mindset : Awareness of emerging technologies and new tooling (good to have)