Description :

- Roles and Responsibilities

- Design, develop, and deploy AI/ML and Generative AI solutions using Python and modern ML frameworks.

- Build, fine-tune, and optimize Large Language Models (LLMs) and multimodal models for text, image, and vision-based use cases.

- Work hands-on with multimodal LLM platforms such as Google Gemini, OpenAI GPT-4V, DALL-E, and LLaVA.

- Develop and implement prompt engineering strategies to improve model accuracy, efficiency, and response quality.

- Design and train machine learning and deep learning models including CNNs, RNNs, Transformers, GANs, and VAEs.

- Perform data preprocessing, feature engineering, cleaning, and augmentation on large structured and unstructured datasets.

- Conduct model training, hyperparameter tuning, cross-validation, and performance evaluation.

- Integrate AI models into production systems using APIs, microservices, and cloud-native architectures.

- Implement MLOps pipelines including CI/CD, model versioning, monitoring, and retraining.

- Mandatory Skills- AI/ML, Python, GENAI and lists mentioned in JD

- Experience on Multimodal LLMs model like Google Gemini, OpenAI GPT-4V (Vision) , DALL-E (OpenAI), LLaVA (Large Language & Vision Assistant), GenAI, Python.

Core Technical Skills :

- Programming: Python (essential), R, Java.

- Math & Stats: Linear Algebra, Calculus, Probability, Statistics.

- Data Handling: Pandas, NumPy for data manipulation; SQL for databases.

- ML/DL Fundamentals: Supervised/Unsupervised Learning, Neural Networks (CNNs, RNNs, Transformers).

- AI/GenAI Specifics: Generative Models (GANs, VAEs), Large Language Models (LLMs), Prompt Engineering.

- Frameworks: TensorFlow, PyTorch, Keras, Hugging Face.

- NLP: SpaCy, NLTK, language models.

- MLOps & Deployment: Docker, Kubernetes, Cloud (AWS, Azure, GCP), CI/CD, Model Monitoring.

- Tools: Git (Version Control), Matplotlib/Seaborn (Visualization).

Core Concepts & Methodologies :

- Algorithm Design: Developing efficient AI algorithms.

- Data Preprocessing: Cleaning, preparing, and augmenting large datasets.

- Model Training & Optimization: Hyperparameter tuning, cross-validation.

- Software Engineering: Agile/Scrum, clean code, system architecture.

Essential Soft Skills :

- Problem Solving: Critical thinking to build innovative solutions.

- Communication: Explaining complex AI concepts to diverse audiences.

- Collaboration: Working in cross-functional teams.

- Adaptability: Staying current with rapidly evolving AI research.

Key Platforms/Tools :

- Cloud: AWS SageMaker, Azure ML, Google AI Platform.

- GenAI Libraries: LangChain, LlamaIndex etc.

Qualifications:

- Qualification Required- B.Sc/ M.Sc in Computer Vision, ML, Statistic