About the Role
We are seeking a highly experienced Senior AI Engineer to design, develop, and deploy cutting-edge AI and data science solutions. You will work on projects involving NLP, LLMs, speech processing, predictive modeling, and intelligent automation. This role requires strong technical ownershipfrom researching algorithms and building ML pipelines to deploying scalable AI services in production.
Responsibilities
- Develop and deploy machine learning and NLP models for production environments
- Build end-to-end AI pipelines, from data ingestion and preprocessing to modeling, evaluation, and deployment
- Fine-tune, evaluate, and integrate Large Language Models (LLMs) into backend systems
- Work with speech technologies (STT, TTS, Whisper) to enable conversational AI and automated communication workflows
- Create scalable API endpoints and microservices that deliver ML functionality to applications
- Design experiments, run model evaluations, and provide data-driven insights to improve accuracy and performance
- Collaborate with engineering teams to ensure AI components integrate seamlessly into broader system architecture
- Optimize models and systems for performance, inference speed, latency, and cost
Requirements
- 4+ years of experience in AI/ML engineering or data science
- Strong experience with Python and ML frameworks (PyTorch, TensorFlow, scikit-learn)
- Hands-on experience with NLP, including text classification, NER, summarization, or conversation systems
- Experience deploying ML models to production environments
- Strong understanding of data science, experimentation, and statistical modeling
- Ability to work independently with high ownership mindset
- Experience with Docker, Kubernetes, and scalable AI deployment patterns
Nice to Have
- Practical experience with LangChain, agentic workflows, or modular LLM frameworks
- Strong knowledge of LLMs, vector embeddings, retrieval-augmented generation (RAG), and prompt engineering
- Experience with speech AI, such as Whisper, TTS engines, voice pipelines, or call-center AI systems
- Familiarity with Twilio, call handling systems, or real-time voice applications
- Proficiency in AWS, Azure, or other cloud platforms for ML deployment
