Associate Consultant, Generative AI

We are growing our Generative AI consulting practice and looking for motivated recent graduates to join as GenAI Consultants. You'll work at the intersection of cutting-edge AI and real business problems — helping clients across industries design, build, and deploy LLM-powered solutions that create tangible value.This is a hands-on technical role. You'll contribute to the full lifecycle of GenAI projects: from architecture and prototyping through to production deployment, evaluation, and iteration. We invest heavily in your development, and expect you to do the same.What You'll DoYou'll design and implement GenAI/LLM solutions leveraging models such as Claude, GPT, Gemini, Llama, and Mistral — selecting the right approach (RAG, agents, fine-tuning, prompt engineering) for each client context. You'll support senior architects in designing scalable, secure, and compliant AI applications while building hands-on experience across the full stack.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).