Principal AI Engineer

Key Responsibilities:

- AI Research & Implementation:

• Read, interpret, and implement state-of-the-art AI/ML white papers into practical, high-impact solutions.
  
  
• Stay updated with the latest advancements in AI, particularly in agentic AI, LLMs, and GenAI applications.
  
  

- Agentic AI Development:

• Design and build agentic AI applications that can autonomously perform tasks, make decisions, and improve workflows.
  
  
• Work with multi-agent systems, memory architectures, reasoning engines, and integration with external tools.
  
  

- Complex Problem Solving in BFSI:

• Solve highly complex problems in BFSI, such as risk assessment, fraud detection, credit scoring, and compliance automation.
  
  
• Develop AI models that optimize decision-making and efficiency in financial services.
  
  

- End-to-End AI System Development:

• Architect, train, and deploy scalable AI/ML models and pipelines.
  
  
• Optimize models for performance, interpretability, and compliance.
  
  

- Leadership & Collaboration:

• Provide technical mentorship to engineers and data scientists.
  
  
• Collaborate with cross-functional teams including product managers, business stakeholders, and engineers.
  
  

- Experimentation & Prototyping:

• Rapidly prototype AI solutions using Python, TensorFlow, PyTorch, LangChain, LlamaIndex, etc.
  
  
• Optimize AI workflows with retrieval-augmented generation (RAG), fine-tuned LLMs, and vector databases.
  
  

Required Skills & Experience

• 8+ years of experience in AI/ML engineering, with a strong research and development background.
  
  
• Expertise in agentic AI frameworks (LangChain, CrewAI, AutoGen, OpenAI agents, etc.).
  
  
• Hands-on experience in building autonomous AI applications that interact with tools, APIs, and environments.
  
  
• Strong experience in LLMs, transformers, embeddings, RAG, and fine-tuning foundation models.
  
  
• Deep understanding of multi-modal AI, reinforcement learning, and probabilistic modeling.
  
  
• Proven track record of implementing AI/ML research papers into production.
  
  
• BFSI domain expertise – experience in financial risk modeling, fraud detection, predictive analytics, etc.
  
  
• Proficiency in Python, PyTorch, TensorFlow, Hugging Face, OpenAI API, LangChain, and cloud AI services (AWS/GCP/Azure).
  
  
• Experience with MLOps, data pipelines, and vector databases (Pinecone, Weaviate, ChromaDB, FAISS).
  
  

Preferred Qualifications

• Publications or patents in AI/ML research.
  
  
• Experience in knowledge graphs, reasoning systems, and symbolic AI.
  
  
• Contributions to open-source AI projects.
  
  
• Background in quantitative finance, computational statistics, or AI in financial markets.