About Syngenta
At Syngenta Crop Protection, we're pioneering solutions that safeguard global food security while championing sustainable agriculture. As a world market leader headquartered in Switzerland, we empower farmers with innovative crop protection technologies that defend against nature's toughest challenges. We unite advanced science with digital solutions to develop intelligent crop protection that maximizes yields while minimizing environmental impact. Join our mission of revolutionizing plant protection from seed to harvest.

At Syngenta, our goal is to build the most collaborative and trustworthy team in agriculture, providing top-quality seeds and innovative crop protection solutions that improve farmers' success. To support this mission, Syngenta’s Seeds P&S is seeking a ML Engineer in Pune.The Machine Learning Engineer designs, develops, and operationalizes scalable machine learning solutions that drive business value. This role requires end-to-end ownership of ML pipelines—from data ingestion and feature engineering to model training, deployment, monitoring, and optimization. The ideal candidate combines strong software engineering practices with deep ML expertise.

Data Engineering & Feature Development: Build and maintain robust ETL/ELT pipelines using Databricks and PySpark. Design and implement feature engineering workflows; leverage Databricks Feature Store for reusability.

Model Development & Experimentation: Develop ML models (regression, classification, clustering, time-series, NLP) using scikit-learn, XG Boost, Light GBM, PyTorch/TensorFlow. Conduct hyperparameter tuning and model selection using ML flow for experiment tracking and reproducibility.

ML Ops & Production Deployment: Deploy models to production using Databricks Model Serving, AWS Sage Maker (if applicable), or custom Fast API/Flask endpoints. Implement CI/CD pipelines for automated testing, validation, and deployment (GitHub Actions, Jenkins, GitLab CI).

Monitoring & Maintenance: Set up model monitoring dashboards to track performance, data drift, and concept drift. Implement alerting systems for anomalies, errors, and SLA breaches.

Collaboration & Documentation: Partner with data scientists, analysts, product managers, and software engineers to translate business problems into ML solutions. Document architecture, workflows, model decisions, and trade-offs (accuracy vs. latency vs. cost).

Core Technical Skills:

Databricks: PySpark, Delta Lake, Databricks SQL, Feature Store, ML flow integration, cluster optimization.

ML flow: Experiment tracking, model registry, model versioning, deployment workflows.

SQL: Complex queries, window functions, CTEs, query optimization, database design.

Python: Expert-level proficiency in Pandas, NumPy, Polars, scikit-learn, XG Boost, Light GBM, PyTorch/TensorFlow.

Version Control: Git, GitHub/GitLab workflows, branching strategies, pull requests.

Required Qualifications:

Education: Bachelor's or Master's in Computer Science, Data Science, Statistics, Engineering, or related field.

Experience: 3–5 years in ML engineering, data engineering, or software engineering with ML focus.

Desired Qualifications:

Certifications: Databricks Certified Machine Learning Professional, AWS Certified Machine Learning – Specialty (optional).

Domain Knowledge: Experience in agriculture, life sciences, or related industries (for Syngenta context).

Advanced Skills: Distributed training, Kubernetes, real-time streaming ML, LLMs/Transformers (Hugging Face)

Syngenta is an Equal Opportunity Employer and does not discriminate in recruitment, hiring, training, promotion or any other employment practices for reasons of race, color, religion, gender, national origin, age, sexual orientation, marital or veteran status, disability, or any other legally protected status. 

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