Bengaluru, Karnataka Principal genai engineer Jobs – 92 Open Jobs Today

Principal Engineer - AI/ML
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GlaxoSmithKline —Bengaluru, Karnataka
- Full-time
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Principal Engineer - AI/ML

GlaxoSmithKline -
Bengaluru, Karnataka

Apply Now

Job details

Full-time
3 days ago

Qualifications

TensorFlow
CI/CD
Azure
Law
Computer Science
Software Engineering
PyTorch
Software deployment
Spark
Git
Master's degree
AWS
Docker
Bachelor's degree
Machine learning
Continuous integration
REST
Unity
GitHub
APIs
Agile
Public relations
Data science
Flask
AI
Communication skills
Python

Full job description

Business Introduction
At GSK, we have bold ambitions for patients, aiming to positively impact the health of 2.5 billion people by the end of the decade. Our R&D focuses on discovering and delivering vaccines and medicines, combining our understanding of the immune system with cutting-edge technology to transform people’s lives. GSK fosters a culture ambitious for patients, accountable for impact, and committed to doing the right thing, making sure that we focus our efforts on accelerating significant assets that meet patients’ needs and have the highest probability of success. We’re uniting science, technology, and talent to get ahead of disease together.
Find out more:
Our approach to R&D

Position Summary

This role is an exciting opportunity to apply your deep expertise in AI/ML engineering to help shape the future of the Global Clinical Operations (GCO) function. You will design, build and operationalize production-grade machine learning solutions on Azure Databricks, MLflow, Unity Catalog and Azure Machine Learning – bringing rigorous engineering discipline to the full model lifecycle from experimentation through to monitored, governed deployment in a regulated environment.

An ideal candidate is passionate about engineering robust, scalable AI/ML systems and extensive hands-on experience deploying, scaling, optimizing models in production at enterprise scale . You will work in agile, cross-functional teams alongside Data Scientists, Data Engineers, Clinical Operations stakeholders and partners from the wider Biostatistics, Data Science and R&D community.

Responsibilities

Architect and deploy production-grade ML models and GenAI/LLM solutions that address real business problems in Clinical Operations, including trial feasibility, site selection, patient recruitment forecasting and study monitoring.

Own the end-to-end ML lifecycle: data ingestion, feature engineering, versioning, serving and performance monitoring in production.
Build, maintain and improve MLOps pipelines on Azure Databricks using Unity Catalog / MLflow Tracking, MLflow Model Registry and automated CI/CD workflows for continuous training and deployment. Manage online/batch endpoints, compute clusters, batch inference pipelines and model monitoring dashboards.
Implement robust model serving patterns including real-time REST endpoints, batch scoring jobs and feature stores.
Collaborate with Data Scientists to translate experimental notebooks into scalable, maintainable, production-ready code following software engineering best practices.
Partner with Data Engineers to ensure feature pipelines, Delta Lake tables and streaming data feeds meet the latency and quality requirements of production models.
Establish and uphold MLOps best practices: experiment tracking, reproducibility, model versioning, A/B testing, canary deployments, drift detection and automated retraining triggers.
Ensure all AI/ML solutions comply with GSK’s data governance, GxP regulations, data privacy standards and responsible AI principles.
Stay current with the latest ML engineering tooling, GenAI advances and cloud platform capabilities; evaluate and introduce new technologies where they add measurable value.
Mentor junior engineers and data scientists on production ML engineering practices and cloud-native Azure/Databricks tooling.

Why You?

This role is based in India and is offered as a hybrid position, combining on-site teamwork and focused remote work. We seek people who deliver practical, reliable AI that drives measurable value. You will join a team focused on learning, inclusion and clear communication. This role offers strong technical growth and meaningful impact on how we use AI across the organisation.

Basic Qualification

We are looking for professionals with the required skills to achieve our goals:

Bachelor’s or master’s degree in computer science, Software Engineering, Mathematics, Statistics or a related quantitative field.
9-10 years of hands-on ML engineering or MLOps experience, with a proven track record of deploying and operating ML models in enterprise production environments.
API Development: Packaging models into scalable microservices using frameworks like FastAPI, Flask, MLFlow / UC model serving endpoint (real-time/batch) etc.
Deep expertise in Azure Databricks: Spark-based data processing, Delta Lake, Databricks Workflows, Databricks / UC Feature Store and cluster lifecycle management.
Proficient use of MLflow for experiment tracking, model registry, model packaging (ML model format) and programmatic deployment via the MLflow REST API.
Strong Python programming skills; solid proficiency in ML frameworks including Scikit-learn, PyTorch and/or TensorFlow; experience packaging models as reusable libraries or containers.
CI/CD experience: Git (branching strategy, PR-based workflows), Azure DevOps or GitHub Actions pipelines for automated testing, model validation and deployment gating.
Containerization with Docker – building, tagging and deploying ML images; familiarity with orchestration concepts relevant to ML workloads.
Working knowledge of Azure cloud data and compute services: ADLS Gen2, Azure Key Vault, Azure Monitor and cost optimization for ML workloads.
Excellent problem-solving and communication skills – ability to articulate technical decisions and trade-offs to non-technical clinical and business stakeholders.
Motivated to operate independently and leading delivery in agile, cross-functional matrix team environments.

What we value in you

Clear, direct communication with technical and non-technical partners.
Practical engineering focus on maintainable, testable solutions.
Curiosity and willingness to learn new models and tools.
Collaborative mindset that encourages inclusion and shared progress.
Commitment to building responsible and trustworthy AI.

How to apply

If this role feels like the right next step, please apply with your CV and a short note about a recent AI/ML project you led. Tell us the problem, your approach and the impact you delivered. We look forward to hearing from you.

Skills

Amazon Web Services (AWS), Artificial Intelligence, Azure Machine Learning, CI/CD, Machine Learning Methods, MLflow

Why GSK?

Uniting science, technology and talent to get ahead of disease together.

GSK is a global biopharma company with a purpose to unite science, technology and talent to get ahead of disease together. We aim to positively impact the health of 2.5 billion people by the end of the decade, as a successful, growing company where people can thrive. We get ahead of disease by preventing and treating it with innovation in specialty medicines and vaccines. We focus on four therapeutic areas: respiratory, immunology and inflammation; oncology; HIV; and infectious diseases – to impact health at scale.

People and patients around the world count on the medicines and vaccines we make, so we’re committed to creating an environment where our people can thrive and focus on what matters most. Our culture of being ambitious for patients, accountable for impact and doing the right thing is the foundation for how, together, we deliver for patients, shareholders and our people.

Inclusion at GSK:

As an employer committed to Inclusion, we encourage you to reach out if you need any adjustments during the recruitment process.

Please contact our Recruitment Team at [email protected] to discuss your needs.

Important notice to Employment businesses/ Agencies

GSK does not accept referrals from employment businesses and/or employment agencies in respect of the vacancies posted on this site. All employment businesses/agencies are required to contact GSK's commercial and general procurement/human resources department to obtain prior written authorization before referring any candidates to GSK. The obtaining of prior written authorization is a condition precedent to any agreement (verbal or written) between the employment business/ agency and GSK. In the absence of such written authorization being obtained any actions undertaken by the employment business/agency shall be deemed to have been performed without the consent or contractual agreement of GSK. GSK shall therefore not be liable for any fees arising from such actions or any fees arising from any referrals by employment businesses/agencies in respect of the vacancies posted on this site.

It has come to our attention that the names of GlaxoSmithKline or GSK or our group companies are being used in connection with bogus job advertisements or through unsolicited emails asking candidates to make some payments for recruitment opportunities and interview. Please be advised that such advertisements and emails are not connected with the GlaxoSmithKline group in any way.

GlaxoSmithKline does not charge any fee whatsoever for recruitment process. Please do not make payments to any individuals / entities in connection with recruitment with any GlaxoSmithKline (or GSK) group company at any worldwide location. Even if they claim that the money is refundable.

If you come across unsolicited email from email addresses not ending in gsk.com or job advertisements which state that you should contact an email address that does not end in “gsk.com”, you should disregard the same and inform us by emailing [email protected] , so that we can confirm to you if the job is genuine.

Apply Now

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Responsibilities

Architect and deploy production-grade ML models and GenAI/LLM solutions that address real business problems in Clinical Operations, including trial feasibility, site selection, patient recruitment forecasting and study monitoring.

Own the end-to-end ML lifecycle: data ingestion, feature engineering, versioning, serving and performance monitoring in production.

Implement robust model serving patterns including real-time REST endpoints, batch scoring jobs and feature stores.

Collaborate with Data Scientists to translate experimental notebooks into scalable, maintainable, production-ready code following software engineering best practices.

Partner with Data Engineers to ensure feature pipelines, Delta Lake tables and streaming data feeds meet the latency and quality requirements of production models.

Establish and uphold MLOps best practices: experiment tracking, reproducibility, model versioning, A/B testing, canary deployments, drift detection and automated retraining triggers.

Ensure all AI/ML solutions comply with GSK’s data governance, GxP regulations, data privacy standards and responsible AI principles.

Stay current with the latest ML engineering tooling, GenAI advances and cloud platform capabilities; evaluate and introduce new technologies where they add measurable value.

Mentor junior engineers and data scientists on production ML engineering practices and cloud-native Azure/Databricks tooling.

We are looking for professionals with the required skills to achieve our goals:

Bachelor’s or master’s degree in computer science, Software Engineering, Mathematics, Statistics or a related quantitative field.

9-10 years of hands-on ML engineering or MLOps experience, with a proven track record of deploying and operating ML models in enterprise production environments.

API Development: Packaging models into scalable microservices using frameworks like FastAPI, Flask, MLFlow / UC model serving endpoint (real-time/batch) etc.

Deep expertise in Azure Databricks: Spark-based data processing, Delta Lake, Databricks Workflows, Databricks / UC Feature Store and cluster lifecycle management.

Proficient use of MLflow for experiment tracking, model registry, model packaging (ML model format) and programmatic deployment via the MLflow REST API.

Strong Python programming skills; solid proficiency in ML frameworks including Scikit-learn, PyTorch and/or TensorFlow; experience packaging models as reusable libraries or containers.

CI/CD experience: Git (branching strategy, PR-based workflows), Azure DevOps or GitHub Actions pipelines for automated testing, model validation and deployment gating.

Containerization with Docker – building, tagging and deploying ML images; familiarity with orchestration concepts relevant to ML workloads.

Working knowledge of Azure cloud data and compute services: ADLS Gen2, Azure Key Vault, Azure Monitor and cost optimization for ML workloads.

Excellent problem-solving and communication skills – ability to articulate technical decisions and trade-offs to non-technical clinical and business stakeholders.

Motivated to operate independently and leading delivery in agile, cross-functional matrix team environments.