About the role
Responsibilities:
- Design, build, and deploy NLP and Generative AI solutions for real-world applications.
- Develop and optimize machine learning and deep learning models, including transformer-based architectures.
- Work extensively with Large Language Models (LLMs), prompt engineering, fine-tuning, RAG pipelines, and agentic workflows.
- Build scalable model pipelines from experimentation to production deployment.
- Collaborate with engineering and product teams to translate business problems into AI solutions.
- Conduct model evaluation, experimentation, benchmarking, and performance optimization.
- Write production-quality Python code and contribute to AI platform architecture.
- Stay up to date with the latest advancements in LLMs, GenAI frameworks, and open-source AI ecosystems.
Requirements:
- 6+ years of experience in Data Science, Machine Learning, or AI Engineering.
- Strong background in Natural Language Processing (NLP) and deep learning.
- Hands-on experience building and deploying ML/AI models in production.
- Strong experience with transformer architectures and modern NLP frameworks.
- Proven experience working with Large Language Models (LLMs) and Generative AI systems.
- Strong coding skills in Python and experience with ML/AI libraries such as PyTorch, TensorFlow, Hugging Face Transformers, LangChain, or similar frameworks.
- Experience with model fine-tuning, embeddings, vector databases, RAG pipelines, and prompt engineering.
- Solid understanding of data structures, algorithms, experimentation, and model evaluation techniques.
- Ability to independently drive projects in ambiguous and fast-paced environments.
Preferred Qualifications:
- Experience with multi-agent systems, AI orchestration, or autonomous workflows.
- Exposure to distributed training, inference optimization, or scalable AI infrastructure.
- Experience deploying AI systems on cloud platforms such as AWS, GCP, or Azure.
- Familiarity with MLOps, CI/CD pipelines, and monitoring production AI systems.
- Research or applied experience in conversational AI, semantic search, summarization, or recommendation systems.
Ideal Candidate:
- The ideal candidate combines strong data science fundamentals with hands-on GenAI engineering expertise.
- You should be comfortable moving from research and experimentation to scalable production systems while maintaining high ownership, speed, and technical rigor.