Job Description
This role is for a Data Science Lead with 10-12 years of experience, including 3+ years in leadership, focusing on AI/ML and conversational AI. The lead will be responsible for architecting solutions, with strong skills in dialogue design, NLU, and various AI platforms like RASA, Kore.ai, and IBM Watson. The position requires expertise in Python, SQL, ML libraries, cloud platforms (Azure, GCP), LLM agent frameworks, prompt engineering, and RAG pipelines.
Must Have:
- 10-12 years in data science, AI/ML, with 3+ years in leadership.
- Proven experience with chatbot development using LLMs and agent frameworks.
- Proficiency in Python, SQL, and key ML libraries (scikit-learn, PyTorch, TensorFlow).
- Deep hands-on experience with Azure (ML, Cognitive Services) and GCP (Vertex AI, AutoML, BigQuery).
- Solid understanding of prompt engineering, tool use chaining, and contextual memory in AI agents.
- Working experience with Agent space.
- Familiarity with vector databases (Pinecone, Weaviate, FAISS) and RAG pipelines.
- Strong communication and stakeholder management skills.
- Expertise in Dialogue Design, Syntax/semantic analysis, Reinforcement learning, Text Classification.
- Experience with RASA, NLU, Kore.ai, GDF, IBM Watson.
- Knowledge of ASR, Dialogue Management (NLG), Spark NLP, Recurrent Neural Networks, Conv AI Testing.
Primary Skills
- Dialogue Design, Syntax and semantic analysis, Reinforcement learning, Text Classification, Governance for Conversational AI, RASA, Natural Language Understanding (NLU), Kore.ai, GDF (Google Dialog Flow), IBM Watson, Automatic Speech Recognition (ASR), Dialogue Management (NLG), Spark NLP, Recurrent Neural Networks, Conv AI Testing
Job requirements
- 10-12 years in data science, AI/ML, or related field, with 3+ years in a leadership capacity. Proven experience with chatbot development using LLMs and agent frameworks (e.g., LangChain, ReAct, OpenAI functions, semantic memory agents). Proficiency in Python, SQL, and key ML libraries (e.g., scikit-learn, PyTorch, TensorFlow). Deep hands-on experience with Azure (e.g., Azure ML, Cognitive Services) and GCP (e.g., Vertex AI, AutoML, BigQuery). Solid understanding of prompt engineering, tool use chaining, and contextual memory in AI agents. Working Experience with Agent space. Familiarity with vector databases (e.g., Pinecone, Weaviate, FAISS) and retrieval-augmented generation (RAG) pipelines. Strong communication and stakeholder management skills.
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