01Overview
Description
- Design and build low-latency AI/ML models for trading signals, market trend prediction, and fraud detection.
- Fine-tune and deploy open-source LLMs (Llama 3, Mistral, Gemma) for market-aware RAG (Retrieval-Augmented Generation) and AI-driven advisory systems.
- Architect and maintain serverless AI pipelines on AWS using SageMaker, Lambda, Fargate, and Bedrock.
- Build and optimize ETL pipelines for high-frequency financial and tick data using AWS Glue and Athena.
- Develop scalable, production-ready GenAI workflows using LangChain, vector databases, and retrieval systems.
- Ensure SEBI-compliant, privacy-preserving, and secure ML pipelines aligned with financial regulations.
- Collaborate closely with product, trading, compliance, and engineering teams to deliver high-impact AI solutions.
- Monitor model performance, latency, drift, and continuously improve system reliability.
Required Skills & Qualifications
- 26 years of hands-on experience in AI/ML, Data Science, or GenAI engineering roles.
- Strong programming skills in Python and SQL.
- Hands-on experience with PyTorch, Hugging Face Transformers, and GenAI frameworks.
- Practical experience with LLM fine-tuning techniques such as LoRA and QLoRA.
- Strong understanding and implementation experience of RAG architectures.
- Experience working with vector databases such as Qdrant or Milvus.
- Solid understanding of real-time or near real-time data processing systems.
- Robust experience with AWS services: SageMaker, Bedrock, Lambda, S3, ECR, Fargate.
- Knowledge of financial markets, trading systems, or fintech workflows is highly desirable.
Tech Stack
- LLMs & GenAI: Llama 3, Mistral, Gemma, LangChain
- Fine-Tuning: LoRA, QLoRA
- Languages: Python, SQL (Bonus: C++)
- ML Frameworks: PyTorch, Transformers
- Vector DBs: Qdrant, Milvus
- Cloud: AWS (SageMaker, Bedrock, Lambda, S3, Glue, Athena, ECR, Fargate)
Nice To Have/Bonus Skills
- Experience with C++ and latency optimization for real-time system .
