AI Engineer

Key Responsibilities

• 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.
  

Required Skills

Key Skills and Qualifications:

• 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.
  

Preferred Skills

• 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.