Description

Key Responsibilities

• Design and develop scalable Gen AI and LLM/GenAI systems for complex business problems
• Build, deploy, and operate production-grade ML and Generative AI services end-to-end
• Build and institutionalize MLOps capabilities, including automated pipelines, monitoring, and model lifecycle management
• Implement multi-modal RAG systems and Agentic AI architectures for enterprise solutions
• Fine-tune and evaluate generative models (e.g., GPT-4.1) for NLP use cases, including summarization and text generation
• Implement real-time model performance monitoring and optimization
• Mentor and uplift junior engineers through design reviews, code reviews, and coaching
• Communicate AI/ML capabilities and results to both technical and non-technical stakeholders

Required Skills

• Strong knowledge of NLP, LLM app development, and Agentic Design
• Experience developing multi-modal RAG systems for enterprise solutions
• Proficiency in Python, R, or Java
• Experience with TensorFlow, PyTorch, Scikit-learn, and OpenAI API
• Cloud platforms: AWS, Azure, Google Cloud Platform, Snowflake, or Databricks
• Containerization: Docker, Kubernetes; microservices architecture
• Understanding of statistics, deep learning, GANs, VAEs, classification, regression, time series, reinforcement learning
• Ability to design Agentic AI architecture, including context engineering and RAG

Preferred Skills

• Expertise in RAG pipeline design and implementation.
• Hands-on knowledge of Chain-of-Thought, Tree-of-Thoughts, Graph-of-Thoughts prompting strategies.
• Familiarity with the financial services industry.
• DevOps practices and Agile methodologies

Key Skills

Education

Bachelor's degree