About the role
At Simplify360, it's more than just work. Redefine the future of customer experiences. One conversation at a time.
Simplify360 is now a Nextiva company. Together, Nextiva and Simplify360 are changing the game with a first-of-its-kind, conversation-centric platform that unifies team collaboration and customer experience in one place. Powered by AI, built by amazing humans.
Our culture is forward-thinking, customer-obsessed and built on an unwavering belief that connection fuels business and life.
If you’re ready to collaborate and create with amazing people, let your personality shine and be on the frontlines of helping businesses deliver amazing experiences, you’re in the right place.
Build Amazing - Deliver Amazing - Live Amazing - Be Amazing
We are seeking a talented and experienced AI/NLP Engineer to join our dynamic team. The ideal candidate will have a strong background in Artificial Intelligence (AI), Natural Language Processing (NLP) and Machine Learning (ML), with proven expertise in fine-tuning, deploying, and optimizing state-of-the-art NLP models. This role demands a hands-on approach to designing, developing, and integrating NLP solutions tailored to solve complex business challenges.
If you are passionate about cutting-edge AI, have a knack for deploying scalable models in production environments, and enjoy collaborating with cross-functional teams, we’d love to hear from you!
Key Responsibilities:
Model Development and Fine-tuning:
Customize, fine-tune, and optimize open-source NLP models (e.g., Transformer-based, Conformer-based, and VITS architecture) using transfer learning.
Develop and train models from scratch when needed to address specific business challenges.
Generative AI & Prompt Engineering:
Design effective prompts for generative AI tasks to meet diverse user and business requirements.
Build similarity models (e.g., symmetric/asymmetric) using BERT, XLNET, and cross-encoders for LLM-based Retrieval-Augmented Generation (RAG) use cases.
Model Deployment & Optimization:
Deploy models at scale using Docker, Kubernetes, and cloud platforms like AWS.
Optimize models for performance using techniques such as vLLM (Paged Attention), quantization, and adapter-based fine-tuning - LoRA, QLoRA.
Performance Monitoring and Enhancement:
Continuously evaluate and enhance model performance to ensure accuracy, scalability, and minimal latency.
Build pipelines for data preprocessing, model serving, and monitoring using tools like Airflow and AWS Batch.
Collaboration:
Work closely with data scientists, software engineers, and product teams to integrate NLP models seamlessly into business applications.
Required Skills:
NLP & Machine Learning:
6+ years of experience in NLP, with expertise in fine-tuning, transfer learning, and developing custom models.
Hands-on experience in ASR, TTS, or custom language model architectures (e.g., Auto-regressive transformers, RWKV).
Generative AI & Prompt Engineering:
Proficiency in designing and crafting prompts for generative AI models aligned with business use cases.
Programming Skills:
Strong proficiency in Python, with experience in implementing NLP models and building pipelines.
Model Deployment:
Hands-on experience with Docker, Kubernetes, and cloud-based deployment on platforms such as AWS.
Deep Learning Frameworks:
Proficiency with TensorFlow, PyTorch, and libraries like Hugging Face Transformers, NLTK, SpaCy, and Gensim.
Bonus Skills:
MLOps & Cloud Services:
Experience with AWS (Lambda, Step Functions, Batch), GCP, Azure, or Nemo for model deployment and pipeline management.
Large-Scale Deployments:
Expertise in scaling AI models and managing SLAs for real-world applications.
Preferred Qualifications:
Bachelor's or Master’s degree in Computer Science, Data Science, Engineering, or a related field.
Proven track record of working with large datasets and creating robust pipelines for data ingestion and preprocessing.
Strong understanding of neural networks and advanced deep learning architectures for NLP.
