Senior/Lead AI/ML Engineer

AI Strategy & Technical Leadership :

- Lead the architecture, design, and implementation of enterprise-scale AI/ML solutions.

- Define and drive the AI/ML roadmap, ensuring alignment with business objectives and product strategy.

- Provide technical leadership and mentorship to AI/ML engineers and data scientists.

- Establish best practices for AI model development, experimentation, deployment, and monitoring.

Generative AI & LLM Systems :

- Design and develop Generative AI applications using LLMs such as GPT, LLaMA, Gemini, or custom models.

- Architect and implement Retrieval-Augmented Generation (RAG) pipelines for enterprise knowledge systems.

- Lead initiatives for LLM fine-tuning, prompt engineering, and model optimization.

- Design AI agent architectures using frameworks like LangChain, LangGraph, and LlamaIndex.

AI/ML Model Development :

- Develop and deploy NLP, Computer Vision, and multimodal AI models for real-world business applications.

- Implement advanced deep learning architectures using PyTorch, TensorFlow, or Keras.

- Identify and evaluate pre-trained and foundation models suitable for specific use cases.

- Drive data preprocessing, feature engineering, and dataset curation for model training.

AI Platform & Infrastructure :

- Design scalable AI infrastructure and MLOps pipelines for model training, deployment, and monitoring.

- Deploy AI solutions across cloud platforms (AWS, Azure, GCP) or hybrid/on-premise environments.

- Build APIs, microservices, and pipelines to integrate AI capabilities into enterprise applications.

- Lead efforts in model optimization, inference acceleration, and resource efficiency.

Performance Optimization & Quality :

- Conduct model evaluation, benchmarking, and continuous performance optimization.

- Optimize AI systems for latency, scalability, and cost efficiency.

- Implement testing, monitoring, and observability frameworks for AI systems in production.

Collaboration & Innovation :

- Work closely with Product, Engineering, and Data teams to define AI-powered product features.

- Stay at the forefront of AI research and emerging technologies, evaluating their business impact.

- Promote a culture of experimentation, innovation, and knowledge sharing within the AI team.