3+ XGBoost Jobs in Chennai | XGBoost Job openings in Chennai

Job Description:

1.     Machine Learning Development & Deployment

·      Design and implement supervised and unsupervised models for predictive analytics, including churn prediction, demand forecasting, renewal risk scoring, and cross sell/upsell opportunity identification.

·      Translate business problems into ML frameworks and production solutions that improve efficiency, revenue, or customer experience.

·      Build, optimize, and maintain ML pipelines using tools such as MLflow, Airflow, or Kubeflow.

2.     Cross-Functional ML Use Cases

·      Partner with teams across Sales (e.g., lead scoring, next-best action), Customer Service (e.g., case deflection, sentiment analysis), Finance (e.g., revenue forecasting, fraud detection), Supply Chain (e.g., inventory optimization, ETA prediction), and Order Fulfillment (e.g., delivery risk modeling) to define impactful ML use cases.

·      Develop domain-specific models and continuously improve them using feedback loops and real-world performance data. 3.

3.      Model Governance and MLOps

·      Ensure robust model monitoring, versioning, and retraining strategies to keep models reliable in dynamic environments.

·      Work closely with DevOps and Data Engineering teams to automate deployment, CI/CD workflows, and cloud-native ML infrastructure (AWS/GCP/Azure).

4. Data Engineering and Feature Architecture

·      Collaborate with data engineers to define feature stores, data quality checks, and model-ready datasets on platforms like Snowflake or Databricks.

·      Perform feature selection, transformation, and engineering aligned with each domain’s business logic. 5. Communication & Stakeholder Collaboration

·      Present technical insights and model results to business and executive stakeholders in a clear, actionable format.

·      Work with Product Owners and Program Managers to scope, prioritize, and plan delivery of ML projects.

Qualifications:

Required

• Bachelor’s or Master’s degree in (e.g., Computer Science, Engineering, Statistics, Mathematics)

• 4+ years of experience in machine learning, data science.

• Proficiency in Python, XGBoost, PyTorch, TensorFlow, or similar.

• Experience deploying models into production using ML pipelines and orchestration frameworks.

• Strong understanding of data structures, SQL, and cloud platforms (e.g., AWS SageMaker, Azure ML, or GCP Vertex AI).

• Hands-on experience in implementing machine learning algorithms such as Random Forest, XGBoost, Logistic Regression, and Deep Learning techniques including Neural Networks (ANN, CNN)

Preferred:

• Experience supporting business functions such as Finance, Sales, or Operations with ML use cases.

• Familiarity with MLOps tools (MLflow, SageMaker Pipelines, Feature Store).

• Exposure to enterprise data platforms (e.g., Snowflake, Oracle Fusion, Salesforce).

• Background in statistics, forecasting, optimization, or recommendation systems.

🎯 Ideal Candidate Profile:

This role requires a seasoned engineer/scientist with a strong academic background from a premier institution and significant hands-on experience in deep learning (specifically image processing) within a hardware or product manufacturing environment.

📋 Must-Have Requirements:

Experience & Education Combinations:

Candidates must meet one of the following criteria:

• Doctorate (PhD) + 2 years of related work experience
• Master's Degree + 5 years of related work experience
• Bachelor's Degree + 7 years of related work experience

Technical Skills:

• Minimum 5 years of hands-on experience in all of the following:
• Python
• Deep Learning (DL)
• Machine Learning (ML)
• Algorithm Development
• Image Processing
• 3.5 to 4 years of strong proficiency with PyTorch OR TensorFlow / Keras.

Industry & Institute:

• Education: Must be from a premier institute (IIT, IISC, IIIT, NIT, BITS) or a recognized regional tier 1 college.
• Industry: Current or past experience in a Product, Semiconductor, or Hardware Manufacturing company is mandatory.
• Preference: Candidates from engineering product companies are strongly preferred.

ℹ️ Additional Role Details:

• Interview Process: 3 technical rounds followed by 1 HR round.
• Work Model: Hybrid (requiring 3 days per week in the office).

Based on the job description you provided, here is a detailed breakdown of the Required Skills and Qualifications for this AI/ML/LLM role, formatted for clarity.

📝 Required Skills and Competencies:

💻 Programming & ML Prototyping:

• Strong Proficiency: Python, Data Structures, and Algorithms.
• Hands-on Experience: NumPy, Pandas, Scikit-learn (for ML prototyping).

🤖 Machine Learning Frameworks:

• Core Concepts: Solid understanding of:
• Supervised/Unsupervised Learning
• Regularization
• Feature Engineering
• Model Selection
• Cross-Validation
• Ensemble Methods: Experience with models like XGBoost and LightGBM.

🧠 Deep Learning Techniques:

• Frameworks: Proficiency with PyTorch OR TensorFlow / Keras.
• Architectures: Knowledge of:
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Long Short-Term Memory networks (LSTMs)
• Transformers
• Attention Mechanisms
• Optimization: Familiarity with optimization techniques (e.g., Adam, SGD), Dropout, and Batch Normalization.

💬 LLMs & RAG (Retrieval-Augmented Generation):

• Hugging Face: Experience with the Transformers library (tokenizers, embeddings, model fine-tuning).
• Vector Databases: Familiarity with Milvus, FAISS, Pinecone, or ElasticSearch.
• Advanced Techniques: Proficiency in:
• Prompt Engineering
• Function/Tool Calling
• JSON Schema Outputs

🛠️ Data & Tools:

• Data Management: SQL fundamentals; exposure to data wrangling and pipelines.
• Tools: Experience with Git/GitHub, Jupyter, and basic Docker.

🎓 Minimum Qualifications (Experience & Education Combinations):

Candidates must have experience building AI systems/solutions with Machine Learning, Deep Learning, and LLMs, meeting one of the following criteria:

• Doctorate (Academic) Degree + 2 years of related work experience.
• Master's Level Degree + 5 years of related work experience.
• Bachelor's Level Degree + 7 years of related work experience.

⭐ Preferred Traits and Mindset:

• Academic Foundation: Solid academic background with strong applied ML/DL exposure.
• Curiosity: Eagerness to learn cutting-edge AI and willingness to experiment.
• Communication: Clear communicator who can explain ML/LLM trade-offs simply.
• Ownership: Strong problem-solving and ownership mindset.