ResponsibilitiesLead Machine Learning EngineerResponsibilitiesLead 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 & ExpertiseCloud & 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 requiredLead Machine Learning EngineerResponsibilitiesLead 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 & ExpertiseCloud & 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)Why join usWe are committed to creating a diverse environment and are proud to be an equal opportunity employer. All qualified applicants receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.Business insightAt Société Générale, we are convinced that people are drivers of change, and that the world of tomorrow will be shaped by all their initiatives, from the smallest to the most ambitious. Whether you’re joining us for a period of months, years or your entire career, together we can have a positive impact on the future. Creating, daring, innovating, and taking action are part of our DNA. If you too want to be directly involved, grow in a stimulating and caring environment, feel useful on a daily basis and develop or strengthen your expertise, you will feel right at home with us!Still hesitating? You should know that our employees can dedicate several days per year to solidarity actions during their working hours, including sponsoring people struggling with their orientation or professional integration, participating in the financial education of young apprentices, and sharing their skills with charities. There are many ways to get involved.We are committed to support accelerating our Group’s ESG strategy by implementing ESG principles in all our activities and policies. They are translated in our business activity (ESG assessment, reporting, project management or IT activities), our work environment and in our responsible practices for environment protection.

Lead ML Engineer - Innovation / Project / Organization - Bangalore, India

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