New opportunity

Project Leader (10961)

Axtria · Noida, India

About this role

The Project Leader role at Axtria is focused on developing and operationalizing scalable machine learning models. The successful candidate will lead mid to large-sized teams, providing mentorship and guidance while delivering high-quality solutions within project timelines. Responsibilities include building, training, and evaluating state-of-the-art machine learning models and managing their deployment. Key technical duties involve utilizing Python data-science packages and various machine learning frameworks to solve complex problems. Candidates should be self-motivated, capable of working in challenging environments, and comfortable using collaboration tools like GIT, Jira, and Confluence. Responsibilities: - Develop and operationalize scalable machine learning models. - Mentor and guide mid to large-sized teams. - Utilize Python and frameworks such as TensorFlow, PyTorch, Keras, and SparkML. - Apply statistical and probabilistic methods including SVM, Decision-Trees, Bagging, Boosting, and Clustering. - Implement core NLP techniques such as Text Classification, NER, Topic Modeling, and Sentiment Analysis. - Collaborate using GIT, Jira, and Confluence. Requirements: - Minimum 3 years of hands-on experience in developing and operationalizing machine learning models. - Strong proficiency in Spark with Scala, Python, or Java. - Experience with cloud platforms (AWS, Azure, or GCP). - Strong understanding of building, training, and evaluating machine learning models. Preferred Qualifications: - Understanding of the Commercial Pharma landscape and Patient Data/Analytics. - Knowledge of Generative AI, Large Language Models, and Transformers.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

PythonNumPyPandasSklearnSeabornPyCaretMatplotlibTensorFlowNLTKStanford NLPPyTorchLing PipeCaffeKerasSparkMLOpenAIGITJiraConfluenceAWSAzureGCPSparkScalaJavaMachine LearningStatistical MethodsProbabilistic MethodsNLPGenerative AI

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Operationalizing Scalable Machine Learning Models
The role requires moving beyond model development to successful deployment in production environments. You must demonstrate proficiency in building, training, and evaluating models using frameworks like TensorFlow, PyTorch, and SparkML, ensuring they are scalable and reliable for business use.
Leadership and Team Mentorship
As a Project Leader, you are responsible for guiding mid to large-sized teams. This involves balancing technical delivery with the development of team members, ensuring high-quality output while maintaining project timelines in a collaborative, tool-driven environment using Jira and Confluence.
Technical Proficiency in NLP and Statistical Methods
You must apply advanced statistical and probabilistic techniques such as SVM, clustering, and boosting, alongside core NLP tasks like NER and sentiment analysis. Mastery of these methods is essential for solving the complex problems typical of the Commercial Pharma landscape.

Likely challenges to think through

Balancing Technical Depth with Project Management
The challenge lies in maintaining hands-on technical excellence while simultaneously managing team dynamics and project deadlines. Prepare by showcasing examples of how you have delegated tasks effectively without losing oversight of the technical architecture or quality standards.
Adapting to Domain-Specific Data Constraints
Working within the Commercial Pharma and Patient Data landscape introduces unique regulatory and data complexity. Prepare by researching common challenges in healthcare analytics, such as data privacy and the specific requirements for model interpretability in a regulated industry.

Practice your answers

Technical practice

Operationalizing Machine Learning Models

What to coverExplain your process for moving a model from a development environment to production. Focus on scalability, monitoring, and maintaining performance metrics once the model is deployed.

Spark and Distributed Computing

What to coverDiscuss your experience using Spark with Scala, Python, or Java for large-scale data processing. Explain how you optimize Spark jobs for performance and handle data partitioning.

NLP Techniques and Implementation

What to coverDescribe how you have implemented core NLP tasks such as NER or Sentiment Analysis. Discuss the trade-offs between using traditional methods versus modern Transformer-based models.

Statistical and Probabilistic Modeling

What to coverCompare the use of Bagging versus Boosting techniques in ensemble learning. Explain how you select the appropriate method based on the specific characteristics of the dataset.

Cloud Platform Integration

What to coverDescribe your experience deploying machine learning solutions on AWS, Azure, or GCP. Focus on how you manage cloud-native services for model storage, compute, and orchestration.

Generative AI and LLMs

What to coverDiscuss your understanding of Large Language Models and Transformers. Suggestion: Prepare to explain how you might integrate LLMs into existing workflows while addressing concerns like latency and cost.

Model Evaluation Strategies

What to coverDetail your approach to evaluating model performance beyond simple accuracy. Discuss how you handle imbalanced datasets and ensure the model generalizes well to new, unseen data.

Python Data Science Ecosystem

What to coverExplain how you leverage libraries like Pandas, NumPy, and Sklearn for feature engineering and data preprocessing. Focus on writing efficient, maintainable code for production-grade pipelines.

Behavioural practice

Mentoring and Team Leadership

What to coverDescribe a situation where you had to mentor a team member or guide a mid-to-large team through a technical challenge. Focus on how you balanced project timelines with the need for team development.

Managing Challenging Environments

What to coverProvide an example of a project where you faced significant technical or environmental obstacles. Explain the specific actions you took to stay self-motivated and deliver the required results.

Collaboration and Tooling

What to coverExplain how you use tools like GIT, Jira, and Confluence to manage project workflows. Describe a time when effective documentation or version control was critical to a project's success.

Handling Tight Timelines

What to coverDescribe a scenario where you had to deliver a high-quality machine learning solution under a strict deadline. Explain how you prioritized tasks and communicated progress to stakeholders.

Sharpen your knowledge

Topics to prepare

Machine Learning Operationalization
Review the end-to-end lifecycle of ML models, focusing on deployment strategies and scalability. Exercise: Draft a high-level architecture diagram for deploying a model into a production environment on a cloud platform, ensuring it addresses monitoring and version control.
Statistical and Probabilistic Modeling
Refresh knowledge on core algorithms including SVM, Decision Trees, Bagging, Boosting, and Clustering. Exercise: Compare the performance and use cases of Boosting versus Bagging techniques for a classification problem to demonstrate technical depth.
Natural Language Processing (NLP) Fundamentals
Deepen understanding of text classification, NER, and sentiment analysis. Exercise: Outline a pipeline for a sentiment analysis project, identifying which specific NLP techniques would be applied at each stage from preprocessing to model evaluation.

Skills to revise

Spark Development
Demonstrate proficiency in Spark using Python, Scala, or Java. Exercise: Write a sample Spark script to perform a data transformation or feature engineering task on a large dataset, highlighting efficient use of Spark RDDs or DataFrames.
Collaboration and Project Management
Utilize GIT, Jira, and Confluence for team coordination. Exercise: Describe a scenario where you managed a project conflict or technical bottleneck using Jira for tracking and Confluence for documentation to ensure team alignment.
Cloud Platform Proficiency
Apply knowledge of AWS, Azure, or GCP to ML workflows. Exercise: Identify the specific services within your chosen cloud provider that would be used to host a trained model and manage its API endpoints.

Your preparation checklist

Prepare a technical project walkthrough
Select a past project where you operationalized a machine learning model. Be ready to explain your choice of framework (e.g., TensorFlow, PyTorch, or SparkML), how you handled model evaluation, and the specific steps taken to deploy the model into a production environment.
Demonstrate Spark and cloud proficiency
Prepare to discuss your experience using Spark with Scala, Python, or Java for large-scale data processing. Detail your experience with AWS, Azure, or GCP, specifically focusing on how you managed cloud-based machine learning pipelines.
Develop a case study on NLP and statistical methods
Create a summary of how you have applied core NLP techniques like NER or Sentiment Analysis, and statistical methods such as Boosting or Clustering, to solve business problems. Highlight how these methods contributed to project outcomes.
Formulate leadership and mentorship examples
Prepare specific examples of how you have mentored team members or led mid-to-large sized teams. Focus on how you managed project timelines, resolved technical roadblocks, and utilized collaboration tools like Jira, Confluence, and GIT to maintain team productivity.
Research the Commercial Pharma landscape
Since knowledge of the Commercial Pharma landscape and patient data is a preferred qualification, research common analytical challenges in this sector. Prepare questions for the interviewer regarding how Axtria uses patient data and Generative AI to solve specific industry problems.

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