AI Lead – Model Fine-Tuning & Federated Learning

Overview

We are seeking an experienced AI Lead to drive the design, development, and deployment of advanced machine learning solutions with a focus on model fine-tuning, personalization at scale, and federated learning frameworks. This role requires a deep understanding of distributed ML systems, privacy-preserving training, and scalable deployment strategies. The AI Lead will collaborate with data engineering and product teams to build enterprise-grade AI systems that balance accuracy, efficiency, and compliance.

Key Responsibilities

• Lead end-to-end model fine-tuning initiatives, adapting foundation models (LLMs, vision models, multimodal models) to domain-specific datasets.
• Architect and implement federated learning pipelines to enable on-device and cross-organization collaboration while maintaining strict data privacy standards.
• Research and apply parameter-efficient fine-tuning techniques (LoRA, adapters, prompt-tuning, quantization) for cost-effective customization.
• Collaborate with data engineering teams to streamline model training across distributed and heterogeneous data sources.
• Drive MLOps practices for monitoring, updating, and retraining fine-tuned models in production environments.
• Evaluate trade-offs between centralized training, decentralized (federated) learning, and hybrid approaches to optimize both performance and compliance.
• Stay updated with advancements in federated optimization algorithms, privacy-preserving techniques (differential privacy, secure aggregation), and model governance frameworks.
• Mentor junior engineers and act as the technical authority for applied AI and federated learning strategies within the organization.

Required Qualifications

• Master’s or PhD in Computer Science, Machine Learning, Artificial Intelligence, or related fields.
• 6+ years of professional experience in machine learning/AI, with at least 2 years leading teams or solutions.
• Strong expertise in model fine-tuning (transformers, diffusion models, or large multimodal models).
• Hands-on experience with federated learning frameworks (TensorFlow, Federated, PySyft, Flower, FedML).
• Deep knowledge of privacy-preserving ML methods: differential privacy, homomorphic encryption, secure MPC.
• Proficiency in PyTorch/TensorFlow, distributed training (Horovod, DeepSpeed), and orchestration tools (Kubernetes, Ray).
• Familiarity with MLOps stack: MLflow, Kubeflow, SageMaker, or Databricks.
• Strong publications, patents, or conference exposure in ML systems or federated learning is a plus.

Preferred Skills

• Experience deploying federated learning at scale in mobile, enterprise, or IoT environments.
• Knowledge of regulatory compliance frameworks for AI (GDPR, HIPAA, data localization laws).
• Strong understanding of edge computing and on-device model optimization (quantization, pruning, distillation).
• Leadership capability to translate research concepts into production-ready enterprise AI products.

Role Impact

The AI Lead will play a pivotal role in bridging cutting-edge research with enterprise adoption, enabling organizations to leverage foundation models without compromising data privacy. This role will accelerate the adoption of federated learning-driven AI systems that scale across industries such as healthcare, finance, and retail.