NLP Engineer

Key Responsibilities

• Lead the design and development of scalable NLP and LLM-powered solutions.
• Architect and optimize RAG pipelines embeddings vector search and LLM applications.
• Fine-tune evaluate and optimize transformer-based models and LLMs for production use.
• Build robust data training inference and model-evaluation pipelines.
• Drive improvements in model quality latency scalability reliability and cost.
• Establish best practices for experimentation evaluation deployment and monitoring of AI systems.
• Translate complex business requirements into effective AI/ML solutions.
• Collaborate closely with ML engineers software engineers product teams and stakeholders.
• Mentor engineers and provide technical leadership across NLP/Generative AI initiatives.
• Stay current with emerging research models frameworks and best practices in NLP and Generative AI.

Requirements

• 5 years of professional experience in NLP Machine Learning or Generative AI.
• Strong Python programming and hands-on experience with PyTorch TensorFlow Hugging Face or equivalent frameworks.
• Deep understanding of NLP transformers LLMs embeddings RAG fine-tuning and model evaluation.
• Proven experience taking AI/ML solutions from experimentation to production.
• Strong software engineering and system-design skills including APIs data pipelines and scalable architectures.
• Strong analytical and problem-solving skills with the ability to independently drive technical initiatives.

Preferred

• Experience with PEFT/LoRA quantization inference optimization or distributed model training.
• Experience with vector databases MLOps cloud platforms and AI evaluation/observability frameworks.
• Experience building AI agents multimodal systems or other advanced Generative AI applications.
• Contributions to open-source projects research publications or applied AI/ML innovation.