Senior Data Analyst

We are looking for a versatile and highly skilled Data Analyst / AI Engineer (Immediate Joiner) to join our innovative team. This unique role combines the strengths of a data scientist with the capabilities of an AI engineer, allowing you to dive deep into data, extract meaningful insights, and then build and deploy cutting-edge Machine Learning, Deep Learning, and Generative AI models. You will play a crucial role in transforming raw data into strategic assets and intelligent applications. Key Responsibilities: Data Analysis & Insight Generation: Perform in-depth Exploratory Data Analysis (EDA) to identify trends, patterns, and anomalies in complex datasets. Clean, transform, and prepare data from various sources for analysis and model development. Apply statistical methods and hypothesis testing to validate findings and support data-driven decision-making. Create compelling and interactive BI dashboards (e.g., Power BI, Tableau) to visualize data insights and communicate findings to stakeholders. Machine Learning & Deep Learning Model Development: Design, build, train, and evaluate Machine Learning models (e.g., regression, classification, clustering) to solve specific business problems. Develop and optimize Deep Learning models, including CNNs for computer vision tasks and Transformers for Natural Language Processing (NLP). Implement feature engineering techniques to enhance model performance. Generative AI Implementation: Explore and experiment with Large Language Models (LLMs) and other Generative AI techniques. Implement and fine-tune LLMs for specific use cases (e.g., text generation, summarization, Q&A). Develop and integrate Retrieval Augmented Generation (RAG) systems using vector databases and embedding models. Apply Prompt Engineering best practices to optimize LLM interactions. Contribute to the development of Agentic AI systems that leverage multiple tools and models. Required Skills & Experience: Data Science & Analytics: Strong proficiency in Python and its data science libraries (Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn). Proven experience with Exploratory Data Analysis (EDA) and statistical analysis. Hands-on experience developing BI Dashboards using tools like Power BI or Tableau. Understanding of data warehousing and data lake concepts. Machine Learning: Solid understanding of various ML algorithms (e.g., Regression, Classification, Clustering, Tree-based models). Experience with model evaluation, validation, and hyperparameter tuning. Deep Learning: Proficiency with Deep Learning frameworks such as TensorFlow, Keras, or PyTorch . Experience with CNNs (Convolutional Neural Networks) and computer vision concepts (e.g., OpenCV, object detection). Familiarity with Transformer architectures for NLP tasks. Generative AI: Practical experience with Large Language Models (LLMs). Understanding and application of RAG (Retrieval Augmented Generation) systems. Experience with Fine-tuning LLMs and Prompt Engineering. Familiarity with frameworks like LangChain or LlamaIndex. Problem-Solving: Excellent analytical and problem-solving skills with a strong ability to approach complex data challenges. Good to Have: Experience with cloud-based AI/ML services (e.g., Azure ML, AWS SageMaker, Google Cloud AI Platform). Familiarity with MLOps principles and tools (e.g., MLflow, DVC, CI/CD for models). Experience with big data technologies (e.g., Apache Spark). Educational Qualification: Bachelor’s degree in Computer Science, Information Technology, or a related field (or equivalent experience). Those who are interested please share your resume to