Lead Data Scientist

Job Description: GEN AI

We are seeking a highly skilled and experienced individual to join our team as an NLP and Generative AI Specialist. As part of our team, you will be responsible for leveraging your expertise in Natural Language Processing (NLP) and Generative Artificial Intelligence (Gen AI) to develop innovative solutions and drive the advancement of our projects.Key Responsibilities:

1. NLP Model Development: Develop, train, and optimize NLP models for various applications such as text classification, sentiment analysis, named entity recognition, and language generation.
2. Design and implement Generative AI models for tasks such as text generation, dialogue generation, and content creation. Development and deployment of RAG applications and SQL based chatbots, and other business use cases
3. Data Preprocessing: Preprocess and clean large datasets for training NLP and Generative AI models, ensuring data quality and consistency.
4. Model Evaluation and Validation: Evaluate the performance of NLP and Generative AI models using appropriate metrics and validation techniques. Continuously refine models to improve accuracy and efficiency.
5. Research and Innovation: Stay updated with the latest advancements in NLP and Generative AI research. Apply cutting-edge techniques and methodologies to solve complex problems and enhance our product offerings.
6. Education: Bachelor's degree or higher in Computer Science, Artificial Intelligence, Machine Learning, or related field. Advanced degree (Master's or Ph.D.) preferred.
7. Experience: Minimum of 2-5 years of professional experience in NLP and Generative AI development. Proven track record of designing, implementing, and deploying successful NLP and Generative AI solutions.
8. Skills:
• Candidate must have excellent communication skills
• Proficiency in programming languages such as Python, TensorFlow, PyTorch, or similar frameworks.
• Strong understanding of NLP techniques and algorithms including word embeddings, sequence modeling, attention mechanisms, and transformer architectures.
• Experience with Generative AI models such as GPT (Generative Pre-trained Transformer) and VAE (Variational Autoencoder).
• Familiarity with data preprocessing techniques, feature engineering, and model evaluation methodologies.

JD - Machine learning (DS):

We are looking for a highly skilled and experienced Machine Learning Engineer / MLOps Engineer to join our team. The ideal candidate will have a strong background in machine learning development and deployment, with a focus on optimizing and scaling machine learning models in production environments. Experience in one or more of the following domains: manufacturing, retail/ecommerce, banking/financial services, or personalization is highly preferred.Key Responsibilities:

1. Machine Learning Model Development: Develop, train, and optimize machine learning models for various applications within the chosen domain(s), such as demand forecasting, fraud detection, recommendation systems, or customer segmentation.
2. Data Engineering: Design and implement data pipelines for ingesting, processing, and transforming large-scale datasets. Ensure data quality, consistency, and reliability for machine learning model training and inference.
3. Model Deployment and MLOps: Implement scalable and reliable infrastructure for deploying machine learning models into production environments. Automate model deployment, monitoring, and maintenance using MLOps best practices and tools.
4. Performance Optimization: Optimize machine learning models and algorithms for performance, scalability, and efficiency. Fine-tune hyperparameters, experiment with different architectures, and implement optimization techniques to improve model accuracy and speed.
5. Model Evaluation and Monitoring: Develop robust evaluation metrics and monitoring frameworks to assess the performance and health of deployed machine learning models. Implement proactive alerting and remediation strategies to address issues in real-time.

Requirements:

1. Education: Bachelor's degree or higher in Computer Science, Engineering, Statistics, Mathematics, or related field. Advanced degree (Master's or Ph.D.) preferred.
2. Experience: Minimum of 4-6 years of professional experience in machine learning development and deployment. Experience in MLOps
3. Technical Skills:
• Proficiency in programming languages such as Python, R, or Scala.
• Strong understanding of machine learning algorithms, statistical modeling, and data analysis techniques.
• Experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, scikit-learn, or Spark MLlib.
• Knowledge of cloud platforms (e.g., AWS, Azure, GCP) and containerization technologies (e.g., Docker, Kubernetes).
• Familiarity with MLOps tools and platforms for model deployment, monitoring, and automation (e.g., MLflow, Kubeflow, TensorFlow Extended).
4. Domain Knowledge: Familiarity with one or more of the following domains: manufacturing, retail/ecommerce, banking/financial services, or personalization. Understanding of domain-specific data sources, challenges, and business objectives is preferred.
5. Communication Skills: Excellent verbal and written communication skills. Ability to effectively communicate complex technical concepts to non-technical stakeholders.

If you meet the above qualifications and are passionate about leveraging machine learning to drive business impact in diverse domains, we encourage you to apply for this exciting opportunity. Join us in shaping the future of AI-driven solutions