Principal AI Engineer

Qualification: Any Engineering/ Computers degree

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.