The Role :

We are looking for a Senior AI Engineer to build the next generation of intelligent trading and advisory systems. You will work at the intersection of Stockbroking and Open Source AI, deploying scalable models on AWS that process millions of transactions and market ticks daily. You will move beyond simple chatbots to build agentic workflows that assist traders, automate compliance, and predict market anomalies in real-time.

Key Responsibilities :

- Real-Time Financial Models: Develop and deploy low-latency models for stock trend prediction, algorithmic trading signals, and fraud detection using PyTorch/TensorFlow.

- Generative AI for Finance: Fine-tune open-source LLMs (Llama 3, Mistral, Gemma) on proprietary financial datasets to build "Market-Aware" RAG systems for research reports and client advisory.

- AWS Cloud Architecture: Architect serverless inference pipelines using AWS SageMaker, Lambda, and Fargate. Optimize costs by utilizing Spot Instances and AWS Inferentia chips.

- Data Engineering: Build robust ETL pipelines using AWS Glue and Athena to process high-frequency tick data and structured financial reports.

- Compliance & Security: Ensure all AI models comply with SEBI regulations regarding data privacy. Implement "Privacy-Preserving ML" techniques to ensure customer data never leaves our secure VPC.

Tech Stack :

- Languages: Python (Advanced), SQL, C++ (Bonus for high-frequency trading optimization).

- Cloud (AWS): SageMaker, Bedrock, Lambda, S3, ECR.

- AI/ML Frameworks: PyTorch, Hugging Face Transformers, LangChain/LlamaIndex, Scikit-learn.

- Vector Databases: Qdrant, Milvus, or AWS OpenSearch.

- DevOps: Docker, Kubernetes (EKS), GitHub Actions for CI/CD.

Experience We Value :

- Financial Domain Knowledge: Understanding of technical indicators (RSI, MACD), Options Greeks, or fundamental analysis ratios.

- Open Source Contributions: A history of contributing to or utilizing open-source AI projects.

- Latency Optimization: Experience quantifying models (Quantization, Pruning) to run on CPU/Edge devices to reduce cloud costs.