Develop and evaluate baseline machine learning models under guidance using appropriate algorithms and metrics.
Support use cases involving classification, regression, clustering, anomaly detection, forecasting, natural language processing, and Generative AI.
Write reusable, readable, secure, and testable Python code aligned with team coding standards.
Help convert notebooks and experiments into modular code, repeatable pipelines, APIs, or scheduled workloads.
Assist with feature engineering, model comparison, hyperparameter tuning, error analysis, and experiment tracking.
Support deployment of models through APIs, containers, batch jobs, or approved cloud services.
Help monitor model quality, data drift, model drift, latency, failures, and operational performance.
Participate in code reviews, pair programming, Agile ceremonies, technical discussions, and knowledge-sharing sessions.
Document datasets, experiments, assumptions, model results, dependencies, limitations, and implementation decisions.
Identify and escalate data privacy, security, compliance, responsible AI, and model-performance risks.
Essential Job Functions
Data Preparation and Exploration
Perform data cleaning, feature preparation, validation, and exploratory analysis.
Use Python libraries such as Pandas, NumPy, Matplotlib, and Scikit-learn.
Document data quality findings and support corrective actions.
Model Development
Build baseline machine learning models and compare performance using suitable evaluation metrics.
Support feature engineering, error analysis, and hyperparameter tuning.
Assist with consumption of foundation models and Large Language Models.
Machine Learning Engineering
Use Git for source control and collaboration.
Package basic applications or models using Docker.
Support APIs, pipelines, experiment tracking, and introductory MLOps practices.
Testing and Monitoring
Write unit tests and data validation checks.
Assist in testing model behavior and production performance.
Support logging, monitoring, troubleshooting, and incident documentation.
Required Qualifications and Skills
Bachelor’s degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Statistics, Mathematics, Engineering, or a related area.
0 to 2 years of relevant experience, including internships, academic projects, research, hackathons, or personal projects.
Working knowledge of Python and SQL.
Understanding of statistics, probability, linear algebra, supervised learning, and unsupervised learning.
Ability to communicate technical findings clearly and work effectively in a collaborative Agile environment.
Strong analytical, problem-solving, documentation, and continuous-learning abilities.
Preferred Skills
Exposure to Azure Machine Learning, Azure AI services, or Azure AI Foundry.
Familiarity with PyTorch, TensorFlow, or similar frameworks.
Exposure to Large Language Models, prompt engineering, embeddings, vector databases, or retrieval-augmented generation.
Familiarity with Claude Code, Codex, or similar agents, used to speed up your own development and to deliver AI-native solutions, with the judgment to review and own what they produce.
Familiarity with Docker, Kubernetes, REST APIs, Linux, GitHub, and MLflow or similar experiment tracking.
Awareness of responsible AI, privacy, explainability, and AI governance.
Mandatory Pre-Joining Technology Training
Selected candidates must complete the assigned role-specific learning plan, hands-on exercises, knowledge checks, and readiness assessment before joining or deployment.
Python programming and object-oriented programming
NumPy, Pandas, data visualization, and SQL
Statistics and probability for machine learning
Supervised and unsupervised machine learning
Model evaluation, feature engineering, and error analysis
Introduction to deep learning and natural language processing
Generative AI, Large Language Models, prompt engineering, embeddings, vector databases, and RAG
Azure Machine Learning, Azure AI services, and Azure AI Foundry
Git, GitHub, Linux, Docker, REST APIs, and testing fundamentals
MLOps, experiment tracking, model monitoring, responsible AI, Agile, and Azure DevOps
Pre-Joining Capstone
Build and demonstrate an end-to-end machine learning solution covering data preparation, model development, evaluation, packaging, documentation, and basic deployment or inference.
We expect the candidate to uphold Crowe’s values of Care, Trust, Courage, and Stewardship. These values define who we are. We expect all of our people to act ethically and with integrity at all times.
Our Benefits
At Crowe, we know that great people are what makes a great firm. We value our people and offer employees a comprehensive benefits package. Learn more about what working at Crowe can mean for you!
How You Can Grow
We will nurture your talent in an inclusive culture that values diversity. You will have the chance to meet on a consistent basis with your Career Coach that will guide you in your career goals and aspirations. Learn more about where talent can prosper!
More about Crowe
Crowe Capability Center – India, operating under the legal entity C3 India Delivery Centre LLP (formerly known as Crowe Horwath IT Services LLP) is a wholly owned subsidiary of Crowe LLP (U.S.A.), a public accounting, consulting and technology firm with offices around the world. Crowe LLP is an independent member firm of Crowe Global, one of the largest global accounting networks in the world. The network consists of more than 200 independent accounting and advisory firms in more than 130 countries around the world.
Crowe does not accept unsolicited candidates, referrals or resumes from any staffing agency, recruiting service, sourcing entity or any other third-party paid service at any time. Any referrals, resumes or candidates submitted to Crowe, or any employee or owner of Crowe without a pre-existing agreement signed by both parties covering the submission will be considered the property of Crowe, and free of charge.
We are committed to a merit-based hiring process, evaluating all candidates consistently using objective, job-related criteria such as relevant experience, demonstrated skills, measurable impact, and alignment with the role’s responsibilities, and making employment decisions in a fair and inclusive manner free from discrimination.
