Gen AI Engineer

We are seeking a skilled Generative AI Engineer with expertise in prompt engineering to design, build, and optimize AI-powered solutions leveraging state-of-the-art generative models such as GPT, LLama, Claude, Gemini and others. This role focuses on crafting effective prompts, fine-tuning models - potentially be involved in fine-tuning efforts, and integrating generative AI capabilities into applications to solve business challenges and enhance user experiences.

As a Generative AI Engineer, you will work closely with cross-functional teams to understand requirements, design AI-driven workflows, and deliver innovative solutions that maximize the potential of generative AI.

Key Responsibilities:

1. Prompt Engineering and Optimization:
- Craft, test, and refine effective prompts to achieve desired outputs from generative models.
- Develop reusable prompt templates for various tasks such as content generation, summarization, translation, or creative design.
- Experiment with prompt tuning and parameter adjustments for optimal performance.

2. Generative AI Solution Development:
- Integrate large language models (LLMs) and generative AI tools into applications and workflows.
- Develop APIs or interfaces for user-friendly access to generative AI capabilities.
- Fine-tune pretrained generative models to align with specific domain requirements.

3. Data Preparation, Model Fine-tuning and Optimization:
- Prepare and preprocess datasets for model training or fine-tuning.

- Potentially be involved in fine-tuning efforts.
- Evaluate and improve models for specific tasks, ensuring reliability and accuracy.

4. Performance Evaluation and Quality Assurance:
- Monitor and evaluate the performance of generative models using metrics like perplexity, BLEU, ROUGE, or custom KPIs.
- Address biases, fairness, inaccuracies, or hallucinations in generated outputs.

5. Collaboration and Documentation:
- Work closely with stakeholders to identify use cases and translate requirements into generative AI solutions.
- Document best practices, workflows, and insights gained during prompt engineering and model optimization.

6. Innovation and Research:
- Stay updated with the latest advancements in generative AI, prompt engineering, and related fields.
- Experiment with emerging models and techniques to push the boundaries of generative AI applications.

Required Skills & Qualifications:

Generative AI Expertise:

- Hands-on experience with generative AI models like GPT (e.g., GPT-4), LLama, Claude, Gemini, or similar.
- Proficiency in crafting, testing, and refining prompts for LLMs to handle diverse tasks effectively.
- Experience with prompt engineering techniques, including prompt-tuning, instruction-based learning, and chain-of-thought prompting.
- Experience with Retrieval-Augmented Generation (RAG) techniques, integrating external knowledge bases with LLMs for enhanced context and accuracy.

Programming Skills:
- Proficiency in Python, including libraries like Transformers (Hugging Face), OpenAI API, LangChain, PyTorch, TensorFlow.
- Experience with integrating AI models into applications through APIs or SDKs.

Model Optimization, Fine-tuning & Deployment:
- Knowledge of transfer learning and fine-tuning methods for pretrained models.
- Experience deploying AI models on cloud platforms (e.g., AWS Sagemaker, Azure AI, Google Vertex AI).
- Familiarity with containerization tools like Docker and orchestration systems like Kubernetes.

Data Preparation and Analysis:
- Proficiency in data preprocessing, annotation, and augmentation techniques.
- Understanding of embeddings and vectorization for tasks like semantic search or similarity scoring.

Collaboration and Communication:
- Ability to work with non-technical stakeholders to design user-centric AI solutions.
- Strong communication skills to explain complex AI outputs and design choices clearly.

Preferred Qualifications:
- Familiarity with reinforcement learning from human feedback (RLHF).
- Knowledge of ethical AI principles and techniques to mitigate biases in generative models.
- Experience with frameworks like LangChain for building AI agents or pipelines.
- Exposure to visualization tools for generative outputs (e.g., streamlit, Gradio).