Technical Lead - Traditional ML

Experience: Not Available to Not Available years

Location: Gurugram, India

Skills: Python, ML framework expertise, TensorFlow, PyTorch, data prep, feature engineering, deep learning, AWS, Azure, GCP, CI/CD, testing, analytical skills, collaboration skills, big data tools, Hadoop, Spark, Docker, Kubernetes, scikit-learn, StatsModels, Apache Kafka, NLP techniques, NLTK, SpaCy, Apache Airflow, Xgboost, Lightgbm, time series analysis, forecasting methodologies, cross-validation, ROC/AUC, Precision/Recall, F1-score, Confusion Matrix

Job Summary
AI Engineer

Key Responsibilities
• Design, develop, and evaluate AI based applications
• Integrate AI capabilities into applications and products
• Research and implement advanced AI techniques like intent, context, search, MCP
• Ability to analyse various models for performance, scalability, and accuracy
• Build and maintain data pipelines
• Monitor and improve model performance in production using various regression and analysis tool like – Promptfoo and DeepEval.
• Document models and promote best practices

Required Skills
• Solid Python and ML framework expertise (TensorFlow, PyTorch, etc.)
• Experience in data prep, feature engineering, and model evaluation
• Knowledge of deep learning
• Cloud experience (AWS/Azure/GCP) and production deployment
• Understanding of software engineering practices (CI/CD, testing)
• Strong analytical and collaboration skills

Desirable
• Familiarity with big data tools (Hadoop, Spark)
• Knowledge of Docker/Kubernetes

Key Responsibilities
1. Develop and implement time series forecasting models using Python, scikit-learn, TensorFlow, and StatsModels,



ensuring robust predictions and model accuracy.
2. Integrate and process large-scale streaming data with Apache Kafka and Spark, enabling real-time feature engineering and data transformation for ML pipelines.
3. Evaluate machine learning models with cross-validation, ROC/AUC, Precision/Recall, F1-score, and Confusion Matrix to ensure optimal performance and reliability.
4. Apply NLP techniques using NLTK and SpaCy to preprocess and analyze textual data for forecasting applications.
5. Optimize model training and deployment workflows using Apache Airflow and Hadoop, maintaining efficiency and scalability in production environments.
6. Collaborate within the development team to advocate and implement coding standards and best practices in Python and ML model development.
7. Prepare technical documentation and status reports to communicate progress, risks, and mitigation strategies for assigned modules.

Skill Requirements
1. Solid Proficiency In Machine Learning And Nlp, Including Supervised And Unsupervised Learning, Deep Learning, And Reinforcement Learning.
2. Solid Experience With Python, Numpy, Pandas, Scikitlearn, Tensorflow, Pytorch, Xgboost, And Lightgbm For Model Development And Evaluation.
3. Solid Understanding Of Time Series Analysis And Forecasting Methodologies.
4. Solid Skills In Apache Spark And Kafka For Distributed Data Processing And Streaming Analytics.
5. Solid Knowledge Of Ml Model Evaluation Metrics And Techniques, Including Crossvalidation And Statistical Analysis.
6. Solid Ability To Apply Nlp Frameworks Such As Nltk And Spacy For Text Data Processing.

Other Requirements
1. Optional But Valuable:
2. Tensorflow Developer Certificate
3. - Apache Spark Developer Certification

📌 Technical Lead Traditional ML (Gurugram)
🏢 HCL Technologies
📍 Gurugram