Compensation: INR 2 crore per year including incentives Strictly please do NOT apply if you have not built 1-2 SLM for clients before. Multiplier AI is a leader in AI accelerators for life sciences and is due for listing. About the Role We are seeking a seasoned and forward-thinking Head for AI and SLM to spearhead Small Language Model (SLM) implementation projects across enterprise and industry-specific use cases. This is a high-impact leadership role that combines deep technical expertise with strategic consulting to deliver scalable, efficient, and secure SLM solutions. Key Responsibilities Lead end-to-end design and deployment of Small Language Models (SLMs) in production environments. Define architecture for on-device or private-cloud SLM deployments, optimizing for latency, token cost, and privacy. Collaborate with cross-functional teams (data, MLOps, product, security) to integrate SLMs into existing systems and workflows. Select and fine-tune open-source or custom SLMs (e.g., Phi-3, TinyLlama, Mistral) for targeted business use cases. Mentor engineering and data science teams on best practices in efficient prompt engineering, RAG pipelines, quantization, and distillation techniques. Act as a thought partner to leadership and clients on GenAI roadmap, risk management, and responsible AI design. Required Skills & Experience Proven experience in deploying Small Language Models in production (not just large-scale LLMs). this is essential do not apply if not done it Strong understanding of transformer architecture, tokenizer design, and parameter-efficient fine-tuning (LoRA, QLoRA). Hands-on with HuggingFace, ONNX, GGUF, and GPU/CPU/edge model optimization techniques. Experience integrating SLMs into real-world systems—mobile apps, secure enterprise workflows, or embedded devices. Background in Python, PyTorch/TensorFlow, and familiarity with MLOps tools like Weights & Biases, MLflow, and LangChain. Strategic mindset to balance model performance vs. cost vs. explainability . Preferred Qualifications Prior consulting experience with AI/ML deployments in pharma, finance, or regulated sectors. Familiarity with privacy-preserving AI, federated learning, or differential privacy. Contributions to open-source LLM/SLM projects. What We Offer Leadership in shaping the future of lightweight AI. Exposure to cutting-edge GenAI applications across industries. Competitive compensation and equity options (for permanent roles). Show more Show less