Generative AI Consultant

Join us as an experienced Generative AI Specialist designing and implementing cutting-edge GenAI/LLM solutions across diverse industries. You'll act as a vital bridge between technical teams (Data Science, ML, Platform) to deliver business-centric AI value. You'll guide clients on the optimal path—using techniques like RAG, agents, or fine-tuning—for cost-effective impact. Your responsibilities extend beyond prompting to architecting robust AI products via benchmarking, prototyping, and validation. You'll orchestrate the full AI workflow, ensuring seamless model integration optimized for performance, security, and scale, while tackling infrastructure challenges. We provide extensive training to support your success. If you're driven to push AI boundaries and help clients rapidly adopt GenAI with confidence, apply to make a real difference. Responsibilities: LLM/GenAI System Development: Design, build, train, fine-tune, and deploy sophisticated AI models leveraging LLMs (e.g., GPT-x, Claude, Gemini, Llama, Mistral) and other generative techniques. Assist in Solution Architecture: Support the GenAI Solution Architect in designing robust, scalable, and secure applications. Application Development: Develop applications powered by GenAI models (both self-managed and API-accessible) that meet business needs and comply with applicable regulations (GDPR, EU AI Act, model licenses, etc.). Advanced Prompt Engineering: Design and optimize effective prompts (e.g., few-shot, Chain/Tree/Graph of Thought, ReAct, Self-reflection, guardrails), balancing simplicity and complexity to enhance analytical capabilities, refine outputs, improve user experience, and control interactions. RAG Implementation: Design and implement Retrieval-Augmented Generation (RAG) architectures to improve accuracy and relevance by retrieving information from pre-determined knowledge sources, providing traceability (source attribution). Model Selection & Fine-Tuning: Select and fine-tune appropriate models (including multimodal - VLM, SLM - Visual Language Models, Small Language Models) to create higher-quality content (text, image, audio, code, etc.) and maximize business value creation opportunities. Integration & Deployment (MLOps): Implement MLOps best practices for the GenAI lifecycle, including automated pipelines (CI/CD), versioning, monitoring, and maintenance in production environments (Cloud platforms like AWS, Azure, GCP). Ensure seamless integration into existing systems and with external tools/APIs, potentially utilizing standardized protocols (MCP). Evaluation & Responsible AI: Develop and execute rigorous evaluation frameworks to measure model performance, reliability, fairness, and safety. Ensure adherence to Responsible AI principles and help teams and clients navigate end-to-end security and compliance processes. Research & Innovation: Stay abreast of the latest advancements in GenAI techniques, technologies, and frameworks. Experiment with new approaches and contribute to internal knowledge sharing. Collaboration: Work effectively within cross-functional teams, communicating complex technical concepts clearly to diverse stakeholders (both technical and non-technical). Documentation: Document processes, methodologies, and best practices for knowledge sharing and future reference. Use Case Differentiation: Distinguish between use cases suited for Generative AI versus traditional NLP applications (e.g., NER, sentiment analysis).