Description
Sybrant Technologies has been in the forefront of transforming its customers into full digital businesses. Though we are small, we grow at a rapid pace due to our capabilities in the contemporary technologies. Sybrant can deep dive in areas such as Mobility, IoT and Analytics in addition to traditional technologies.We can rapidly implement these solutions because of the Products, Frameworks and Partnerships that we have. In addition, our technically sound people and proven processes help in accelerating our customers’ adoption curves. Advantage Sybrant has always been in its nimbleness and delivering high quality yet cost effective solutions. That’s why we are the “Digital Transformation Power” behind our customers. We are a PreludeSys Group Company.
Role:
Design and build advanced conversational AI agents that engage in human-like dialogue using Python and modern NLP/LLM technologies.
Requirements
- Develop conversational AI agents that handle multi-turn dialogues, context retention, and personalized responses
- Use Python with frameworks like LangChain, AutoGen, Rasa, or OpenAI’s Assistants API to build dialogue systems
- Integrate LLMs (GPT-4, Claude, Gemini, Llama 3) with speech-to-text/text-to-speech (TTS/STT) for voice-enabled agents (if needed)
- Implement memory & context management (e.g., using vector DBs like Pinecone or Redis)
- Optimize conversational flows using prompt engineering, fine-tuning, or RAG (Retrieval-Augmented Generation).
- Deploy agents on messaging platforms (Slack, WhatsApp, Teams) or voice assistants (Alexa, Google Assistant).
Required Skills:
- Strong Python programming (async, APIs, Flask/FastAPI)
- Experience with AI agent frameworks (LangChain, AutoGen, CrewAI, etc.)
- Knowledge of LLM integration (OpenAI, Anthropic, Mistral, etc.)
- Familiarity with API interactions (REST, GraphQL
- Apply prompt engineering strategies to guide and control LLM outputs
Good to Have (Added Advantage):
- Experience with RAG (Vector DBs like Pinecone, FAISS, Weaviate)
- Knowledge of vector databases (for chatbot memory)
- Deployment on cloud platforms (AWS Lambda, GCP, Azure Bot Service)