Senior AI/ML Engineer

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Job Description

The Future Begins Here.

At Takeda, we are leading digital evolution and global transformation. By building innovative solutions and future-ready capabilities, we are meeting the needs of patients, our people, and the planet.

Bengaluru, India’s epicenter of Innovation, has been selected to be home to Takeda’s recently launched Innovation Capability Center. We invite you to join our digital transformation journey. In this role, you can boost your skills and become the heart of an innovative engine contributing to global impact and improvement.

At Takeda’s ICC, we Unite in Diversity.

Takeda is committed to creating an inclusive and collaborative workplace where individuals are recognized for their backgrounds and abilities. We are continuously improving our collaborators’ journey at Takeda, and we welcome applications from all qualified candidates. Here, you will feel welcomed, respected, and valued as an essential contributor to our diverse team.

Job Responsibilities

• Act as a trusted advisor and an internal Gen AI consultant to functional stakeholders and internal Takeda Business Units and Business Functions for data science activities.
• Serve as a Lead for Gen AI use cases and operationalization on complex projects with diverse scope, including primary contact for internal liaison with Global Data, Team, and other parts of the GDD&T and BU/BF functions.
• Designed and built RAG service platform orchestrations, including prompt engineering, guardrails, vector databases, and API Grounding.
• Design, develop, and implement MLOps pipelines for Generative AI models, covering data ingestion, preprocessing, training, deployment, and monitoring.
• Automate ML tasks using GitOps, CI/CD pipelines, and containerization tools like Docker and Kubernetes.
• Design and drive the development of models and analytical tools and productionize them within models hosted in Azure Open AI, AWS Sagemaker, Salesforce, or equivalent large language models on a modern containerized deployment stack.
• Creates database-to-deployment pipelines for models using the necessary programming languages (primarily R, Python, SQL) and tools (AWS SageMaker, Dataiku, Databricks, etc.). This is a hands-on role; you must code 50%-70% of your time.
• Performs business readiness check, data access and quality check, and hands-on exploratory data analysis to gauge the need for or appropriateness of analytical modeling.
• Acts as central steward of data quality monitor risks through the holistic review of clinical and operational data, uses detailed knowledge of the protocol, considers the specific therapeutic area aspects related to the data collected, and aligns with cross-functional operational plans to drive comprehensive data science modeling tasks.
• Partner with the centralized Demand Management team and GDD&T to monitor and communicate project progress to the sponsor and project team, including using project status reports and tracking tools/metrics.
• Manage external ML engineers and vendors responsible for building and maintaining ML Pipeline solutions.
• Performs other work-related duties as assigned. Minimal travel may be required (up to 25%)
  
  

Qualifications

Education

Bachelor's degree in Technical Disciplines (Computer Science, Data Science, Information Technology, Economics, Physics, Mathematics or equivalent). A master's or PhD degree is highly preferred.

Work Experience & Technical Requirements

• Overall 5+ years of experience in data, business insights, and ML experience in Life Sciences, primarily in commercial (sales, marketing, medical, and access) functions.
• Deep understanding of modern data technologies, data architecture principles, AI/machine learning, GenAI, and similar technologies.
• For a master's or a PhD, a minimum of 3 years of Machine Learning or Data Science experience in Technology companies, Life Sciences, Pharma, or Biotech is expected.
• Knowledge of medical terminology, clinical data, and commercial pharma data sources for clinical studies, particularly requirements applicable to Clinical Data Sciences.
• Experience with Generative AI, including transformers or LLM project work with frameworks, including Huggingface, LangChain, or GPT
• 2+ years of experience applying large language and generative AI models, including transformers and GPT models.
• Experience in building chatbots, retrieval augmented generation, and finetuning LLMs.
• Experience building production-grade ML/automation pipelines from scratch (model versioning, lineage, monitoring, deployment, optimization, scalability, orchestration, and continuous learning).
• Proficient in Python, SQL, Git, and Jenkins and in frameworks such as sci-kit-learn, Keras, PyTorch, Tensorflow, etc.
• Experience in using MLOps frameworks like Kubeflow, MLFlow, and DataRobot.
• Experience with DataBricks, Dataiku AWS SageMaker, Azure, and data pipeline management tools like Airflow.
• Minimum 3 years of experience automating ML build, training, evaluating, and deploying ML models.
• Minimum 3 years of experience designing and implementing cloud solutions (AWS, Azure, or GCP).
• Knowledge of pharma commercial analytical models and domains.
• Self-starter with the ability to work independently.
• Strong team player with excellent communication skills.
• Exceptional troubleshooting and problem-solving abilities.
  
  

Nice To Have Technical Skills

• Cloud Services AWS (EC2, S3, Lambda, RDS, DynamoDB, CloudFormation), Azure (VMs, Blob Storage, Functions, Cosmos DB, DevOps), Databricks (Delta Lake, MLflow).
• Programming Python, Scala
• ML & Gen AI Frameworks TensorFlow, PyTorch, LangChain.
• Orchestration Docker, Airflow.
• AWS Services Compute (EC2, Lambda), Storage (S3, EFS), Database (RDS, DynamoDB), Networking (VPC, Route 53), Security (IAM, KMS), Management (CloudWatch, CloudFormation).
• ETL & Pipelines AWS Glue, Apache Spark, Kafka.
• Storage S3, Redshift, RDS.
• Model Deployment AWS SageMaker (Build, Train, Deploy), AWS Bedrock (Foundation Models, Customization).
• CI/CD AWS CodePipeline, CodeBuild, CodeDeploy, Jenkins, GitLab CI/CD.
  
  

You may not fit every criterion we seek, but you must still be the right fit for the role. You may have experience outside their requirements that makes you a great candidate. Please consider applying even if you do not meet the abovementioned requirements.

What Takeda Can Offer You

• Takeda is certified as a Top Employer, not only in India, but also globally. No investment we make pays greater dividends than taking good care of our people.
• At Takeda, you take the lead on building and shaping your own career.
• Joining the ICC in Bengaluru will give you access to high-end technology, continuous training and a diverse and inclusive network of colleagues who will support your career growth.
  
  

Benefits

It is our priority to provide competitive compensation and a benefit package that bridges your personal life with your professional career. Amongst our benefits are

• Competitive Salary + Performance Annual Bonus
• Flexible work environment, including hybrid working
• Comprehensive Healthcare Insurance Plans for self, spouse, and children
• Group Term Life Insurance and Group Accident Insurance programs
• Health & Wellness programs including annual health screening, weekly health sessions for employees.
• Employee Assistance Program
• 3 days of leave every year for Voluntary Service in additional to Humanitarian Leaves
• Broad Variety of learning platforms
• Diversity, Equity, and Inclusion Programs
• Reimbursements – Home Internet & Mobile Phone
• Employee Referral Program
• Leaves – Paternity Leave (4 Weeks) , Maternity Leave (up to 26 weeks), Bereavement Leave (5 calendar days)
  
  

About Icc In Takeda

• Takeda is leading a digital revolution. We’re not just transforming our company; we’re improving the lives of millions of patients who rely on our medicines every day.
• As an organization, we are committed to our cloud-driven business transformation and believe the ICCs are the catalysts of change for our global organization.
  
  

Locations

IND - Bengaluru

Worker Type

Employee

Worker Sub-Type

Regular

Time Type

Full time