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Role Overview
We are looking for an NLP Engineer to lead the design, development, and deployment of production-grade NLP and Generative AI solutions. You will own complex AI initiatives end-to-end, drive technical architecture, and help shape our NLP/LLM engineering practices.
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
