About the role
- Develop and deploy AI/ML models with a focus on NLP, Generative AI, RAG, and Time Series Forecasting workflows.
- Engineer and refine prompts to optimize performance for large language models (LLMs) and generative AI applications.
- Implement and maintain data preprocessing pipelines, including data cleansing, feature engineering, embedding generation, and transformation for time series data.
- Develop end-to-end solutions integrating LLMs with VectorDB (e.g., Weaviate, Pinecone, FAISS) for document retrieval, semantic search, and contextual query answering.
- Utilize LangGraph and other Generative AI frameworks to build structured workflows for LLM-based applications.
- Train and fine-tune machine learning models, including LLMs, to optimize performance for various use cases.
- Analyze and interpret complex datasets, including time series data, ensuring scalable and efficient AI model deployment.
- Collaborate with cross-functional teams to integrate AI/ML models into existing systems and workflows.
- Ensure models are robust, scalable, and adhere to best practices in ethical AI development.
- Conduct testing, performance benchmarking, and iterative refinements of AI models and pipelines.
- Stay updated with the latest advancements in AI/ML, NLP, and cloud technologies and recommend integration strategies for emerging tools and techniques.
- Create technical documentation and presentations to effectively communicate AI concepts to stakeholders.
- AI/ML Expertise: Proven experience in developing, fine-tuning, and deploying AI/ML models, focusing on NLP, Generative AI, and Time Series Forecasting.
- Prompt Engineering: Proficiency in designing, testing, and optimizing prompts for LLMs.
- Data Analysis: Strong ability to process and interpret large and complex datasets, including time series data, for AI model training and validation.
- Programming: Proficiency in Python and experience with AI/ML frameworks like TensorFlow, PyTorch, and scikit-learn.
- NLP Techniques: Understanding of tokenization, embeddings, transformer-based models (e.g., BERT, GPT, LLaMA), and RAG workflows.
- Vector Databases: Experience with Weaviate, Pinecone, FAISS, or similar VectorDBs for document retrieval and storage.
- LangGraph & GenAI Frameworks: Hands-on experience with LangGraph, LangChain, HuggingFace Transformers, OpenAI API, and other Generative AI tools.
- Cloud Deployment: Familiarity with deploying AI solutions on AWS, GCP, or Azure.
- Time Series Forecasting: Experience with ARIMA, LSTM, Prophet, and other forecasting techniques.
- Model Training & Fine-Tuning: Ability to train and fine-tune machine learning models, including large language models (LLMs), to enhance performance and accuracy.
- Problem-Solving & Collaboration: Strong analytical, problem-solving, and teamwork skills to work effectively in a cross-functional environment.
- Experience in implementing RAG workflows and integrating generative AI with retrieval-based systems.
- Familiarity with ethical AI principles and compliance frameworks.
- Knowledge of additional programming languages such as JavaScript or SQL.
- Proficiency in MLOps practices for automating AI/ML pipelines and lifecycle management.
- Experience in developing AI-driven applications for real-world industry use cases.