Data Scientist ML Platforms

Pune - India

Monthly Salary: Not Disclosed

Posted on: 4 hours ago

Vacancies: 1 Vacancy

Job Summary

As a Data Scientist in our MLOps Platform Engineering team you will contribute to AI Enablement by building and operating production-grade machine learning and LLM systems. You will collaborate with cross-functional teams to implement scalable data pipelines training and fine-tuning workflows model orchestration inference mechanisms and monitoring/evaluation solutions for both classical ML and modern LLM applications.

Responsibilities:

Design and implement scalable maintainable data pipelines for ingestion transformation model training and retraining under established architectural patterns.
Implement orchestration frameworks for ML/LLM and support hybrid model deployment at scale.
Develop and scale inference services and retrieval-augmented generation (RAG) systems.
Integrate with various Internal/External APIs embeddings and data connectors
Drive monitoring observability evaluation and explainability for machine learning and LLM systems.
Contribute towards building robust CI/CD pipelines using standard DevOps/MLOps practices.
Collaborate with engineering product and research teams to operationalise and productionize AI/ML/LLM solutions.
Write clear technical documentation and best practices guidelines.
Knowledge share and contribute to developer knowledge forums

Must-Have Skills and Experience:

5 years in Data Science ML Engineering or related roles with hands-on experience in productionizing ML/LLM solutions.
Strong programming skills in Python; With understanding and experience with distributed computing platforms like Apache Spark etc.
Demonstrate a strong understanding of core machine learning algorithms and their mathematical foundations.
Proficiency in building performant and robust data engineering pipelines (ETL/ELT) and large-scale data infrastructure (e.g. Data Lake Data Mesh Databricks).
Expertise with machine learning lifecycle tools and orchestration frameworks (MLflow Kubeflow EKS etc.).
In-depth understanding and expertise in the different phases of ML Lifecycle challenges and mitigating approaches
Extensive experience in classical ML and LLM model development fine-tuning and deployment orchestration monitoring and observability.
Hands-on with cloud platforms and services (AWS Azure GCP) including storage (S3 Data Lake) and compute.
Deep understanding of MLOps/DevOps including CI/CD experiment tracking monitoring and version control (Git).
Demonstrated ability to integrate multiple data types: structured unstructured image voice and geospatial.
Demonstrate ability to integrate multimodal datasets
Strong communication documentation and stakeholder management skills.

Nice-to-Have:

Experience in deploying AI/ML capabilities to front-end applications (web/mobile).
Familiarity with RLHF prompt engineering PEFT/LoRa/QLoRa methods for LLM tuning.
Appreciation for AI Agents and their system-level interaction with ML/LLM models
Knowledge of or experience with Graph-based approaches like Graph-RAG

Qualifications :

Bachelors or Masters degree in Computer Science Statistics Mathematics Data Science or a related quantitative field.

Additional Information :

Note: 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 gender identity marital or veteran status disability or any other legally protected status.

LI page - Work :

Employment Type :

Full-time

Responsibilities:

Design and implement scalable maintainable data pipelines for ingestion transformation model training and retraining under established architectural patterns.
Implement orchestration frameworks for ML/LLM and support hybrid model deployment at scale.
Develop and scale inference services and retrieval-augmented generation (RAG) systems.
Integrate with various Internal/External APIs embeddings and data connectors
Drive monitoring observability evaluation and explainability for machine learning and LLM systems.
Contribute towards building robust CI/CD pipelines using standard DevOps/MLOps practices.
Collaborate with engineering product and research teams to operationalise and productionize AI/ML/LLM solutions.
Write clear technical documentation and best practices guidelines.
Knowledge share and contribute to developer knowledge forums

Must-Have Skills and Experience:

5 years in Data Science ML Engineering or related roles with hands-on experience in productionizing ML/LLM solutions.
Strong programming skills in Python; With understanding and experience with distributed computing platforms like Apache Spark etc.
Demonstrate a strong understanding of core machine learning algorithms and their mathematical foundations.
Proficiency in building performant and robust data engineering pipelines (ETL/ELT) and large-scale data infrastructure (e.g. Data Lake Data Mesh Databricks).
Expertise with machine learning lifecycle tools and orchestration frameworks (MLflow Kubeflow EKS etc.).
In-depth understanding and expertise in the different phases of ML Lifecycle challenges and mitigating approaches
Extensive experience in classical ML and LLM model development fine-tuning and deployment orchestration monitoring and observability.
Hands-on with cloud platforms and services (AWS Azure GCP) including storage (S3 Data Lake) and compute.
Deep understanding of MLOps/DevOps including CI/CD experiment tracking monitoring and version control (Git).
Demonstrated ability to integrate multiple data types: structured unstructured image voice and geospatial.
Demonstrate ability to integrate multimodal datasets
Strong communication documentation and stakeholder management skills.

Nice-to-Have:

Experience in deploying AI/ML capabilities to front-end applications (web/mobile).
Familiarity with RLHF prompt engineering PEFT/LoRa/QLoRa methods for LLM tuning.
Appreciation for AI Agents and their system-level interaction with ML/LLM models
Knowledge of or experience with Graph-based approaches like Graph-RAG

Qualifications :

Bachelors or Masters degree in Computer Science Statistics Mathematics Data Science or a related quantitative field.

Additional Information :

LI page - Work :

Employment Type :

Full-time

Key Skills

Laboratory Experience
Immunoassays
Machine Learning
Biochemistry
Assays
Research Experience
Spectroscopy
Research & Development
cGMP
Cell Culture
Molecular Biology
Data Analysis Skills

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About Company

Syngenta Group

To help feed 10 billion people while reducing emissions and improve biodiversity. This is our mission as the global agriculture technology leader. With 59,000 employees in more than 100 countries and hundreds of thousands of agricultural partners worldwide, we are committed to transfo ... View more

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