NLP Engineer

Role Summary

We are seeking a seasoned NLP Engineer to spearhead the design, creation, and deployment of production-level NLP and Generative AI technologies. The role involves end-to-end ownership of sophisticated AI projects, defining technical architecture, and shaping engineering methodologies for NLP and large language models (LLMs).

Key Responsibilities

• Lead the architecture and construction of scalable solutions powered by NLP and LLMs.
• Design and improve retrieval-augmented generation (RAG) pipelines, embeddings, vector search mechanisms, and LLM implementations.
• Perform fine-tuning, assessment, and optimization of transformer architectures and LLMs for use in production environments.
• Develop robust pipelines covering data preparation, training processes, inference, and model evaluation.
• Enhance model performance in terms of quality, latency, scalability, reliability, and cost-effectiveness.
• Define and implement best practices for experimentation, evaluation, deployment, and ongoing monitoring of AI solutions.
• Interpret intricate business needs to develop efficient AI and machine learning systems.
• Work collaboratively with machine learning engineers, software developers, product teams, and other stakeholders.
• Provide mentorship and technical guidance to engineers engaged in NLP and Generative AI projects.
• Keep up to date with the latest research, new models, frameworks, and industry best practices in the NLP and Generative AI domain.

Requirements

• At least 5 years of professional experience specializing in NLP, machine learning, or Generative AI.
• Proficient in Python programming with hands-on expertise in frameworks such as PyTorch, TensorFlow, or Hugging Face.
• Comprehensive knowledge of NLP concepts, transformers, LLMs, embeddings, RAG techniques, fine-tuning, and model evaluation metrics.
• A proven track record of deploying AI and ML solutions from prototype to production environments.
• Strong background in software engineering and system design encompassing APIs, data pipelines, and architectures that support scaling.
• Excellent analytical and problem-solving capabilities to independently lead technical projects.

Preferred Qualifications

• Experience using parameter-efficient fine-tuning methods such as PEFT/LoRA, quantization techniques, inference optimizations, or distributed model training systems.
• Familiarity with vector databases, MLOps practices, cloud infrastructure, and AI evaluation or observability tools.
• Background building AI agents, multimodal systems, or other advanced applications within Generative AI.
• Contributions to open-source projects, research publications, or innovation in applied AI/ML fields.