What we are looking from an ideal candidate?

1. • Data Exploration and Analysis: Dive deep into raw data to frame insightful questions, identify trends, and deliver actionable solutions.
   • Generative AI Development: Design, fine-tune, and deploy Generative AI models like GPT, Stable Diffusion, and DALL·E for innovative applications, including content generation, AI-powered recommendations, and creative problem-solving.
   • Large Language Models (LLMs): Build and optimize LLMs, leveraging frameworks like LangChain to enhance AI capabilities for conversational agents and other real-world applications.
   • Model Development: Develop predictive models and classifiers using mathematical modeling, machine learning, and statistical techniques.
   • Data Preprocessing: Perform data cleansing, transformation, and preparation of structured and unstructured datasets to uncover patterns and enable advanced analytics.
   • AI Integration: Work on integrating Generative AI and advanced AI techniques into scalable systems, ensuring impactful solutions for customer challenges.
   • Data Visualization: Create interactive dashboards and visualizations using tools like Tableau, Power BI, or Python libraries (e.g., Seaborn, Plotly) to communicate insights effectively.
   • Advanced Applications: Apply computer vision techniques such as preprocessing, feature extraction, and pattern recognition, and experiment with NLP and image generation methodologies.
   • Collaborative Development: Work closely with software engineers and architects to extract, transform, and standardize data for analytics and machine learning workflows.
   • MLOps Practices: Support the creation and maintenance of automation pipelines for model training, deployment, and monitoring using tools like MLflow, Docker, and Kubernetes.
   • Innovation and Strategy: Continuously explore advancements in Generative AI and propose innovative strategies to address complex business problems.

Preferred Skills:

What skills do you need?

1. 1. • B.Tech/MS/M.Tech or PhD in Computer Science, Machine Learning, AI, or related field.
      • Hands-on experience or familiarity with Generative AI models like GPT, DALL·E, MidJourney, and tools such as Hugging Face and OpenAI APIs.
      • Exposure to Large Language Models (e.g., BERT, T5) and tools like LangChain, RAG pipelines, or similar frameworks.
      • Understanding of supervised, unsupervised, and reinforcement learning, and expertise in algorithms like XGBoost, random forests, and deep learning models.
      • Experience with tokenization, embeddings, sentiment analysis, and sequence-to-sequence models.
      • Proficiency in ETL processes, data pipelines, and experience with Big Data tools like Apache Spark, Kafka, or Hadoop.
      • Strong knowledge of Tableau, Power BI, or Python visualization libraries (Seaborn, Matplotlib, Plotly).
      • Familiarity with MLOps tools like MLflow, Kubeflow, Docker, Kubernetes, Jenkins, and CI/CD pipelines.
      • Experience with cloud environments like AWS (SageMaker), Azure (Machine Learning Studio), or Google Cloud (Vertex AI).
      • Solid foundation in linear algebra, calculus, probability, and statistics, with an ability to apply these concepts to AI models.
      • Strong skills in SQL for relational databases and familiarity with NoSQL systems (e.g., MongoDB, Neo4j).
      • Knowledge of transformer-based models and their application in NLP, vision, and multimodal tasks.
      • Experience with preprocessing, feature extraction, pattern recognition, and frameworks like OpenCV or YOLO.
      • Ability to review academic papers and implement cutting-edge techniques.
      • Strong analytical thinking, problem-solving, teamwork, and communication skills.
      • Exposure to Generative Adversarial Networks (GANs), VAEs, and similar frameworks.
      • Contributions to open-source projects or personal AI portfolios on platforms like GitHub.
      • Awareness of emerging AI ethics and responsible AI practices.
      • Knowledge of time-series analysis, anomaly detection, or geospatial data processing.
      • Experience with hybrid or federated learning approaches.

Note: Candidates without hands-on experience or practical exposure to the latest AI trends and technologies are encouraged not to apply