Lead Assistant Manager

Lead Assistant Manager – Data Scientist (NLP & Generative AI)

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About The Role

Key Responsibilities

• Lead the architecture, development, and deployment of scalable machine learning and AI systems centered on real-time LLM inference for concurrent users.
• Design, implement, and manage agentic AI frameworks leveraging Google Adk, Langgraph or custom-built agents.
• Integrate foundation models (GPT, LLaMA, Claude, Gemini) and fine-tune them for domain-specific intelligent applications.
• Build robust MLOps pipelines for end-to-end lifecycle management of models—training, testing, deployment, and monitoring.
• Collaborate with DevOps teams to deploy scalable serving infrastructures using containerization (Docker), orchestration (Kubernetes), and cloud platforms.
• Drive innovation by adopting new AI capabilities and tools, such as Google Gemini, to enhance AI model performance and interaction quality.
• Partner cross-functionally to understand traffic patterns and design AI systems that handle real-world scale and complexity.
  

Required Skills & Qualifications

• Bachelor’s or Master’s degree in Computer Science, AI, Machine Learning, or related fields.
• 5+ years in ML engineering, applied AI, or data scientist roles.
• Strong programming expertise in Python and frameworks including PyTorch, TensorFlow, Hugging Face Transformers.
• Deep experience with NLP, Transformer models, and generative AI techniques.
• Hands-on experience deploying AI models to concurrent users with high throughput and low latency.
• Skilled in cloud environments (AWS, GCP, Azure) and container orchestration (Docker, Kubernetes).
• Familiarity with vector databases (FAISS, Pinecone, Weaviate) and retrieval-augmented generation (RAG).
• Experience with agentic AI using Adk, LangChain, Langgraph and Agent Engine
  

Preferred Qualifications

• Experience with Google Gemini and other advanced LLM innovations.
• Contributions to open-source AI/ML projects or participation in applied AI research.
• Knowledge of hardware acceleration and GPU/TPU-based inference optimization.