Key Responsibilities:
End-to-End ML & Full-Stack Ownership
1. Technical Expertise:
- Strong background in data science, with deep knowledge of machine learning (ML), deep learning (DL), reinforcement learning (RL), self-supervised/unsupervised learning, LLM/Agentic AI.
- Hands-on experience in recommendation systems, NLP/LLMs, risk management, Agentic AI Application or related fields.
2. Full-Stack Development:
- Architect, develop, and deploy ML-powered applications, from data pipelines to frontend interfaces.
- Own end-to-end ML module/product implementation, integrating models into scalable backend services (APIs, microservices) and frontend dashboards.
3. System Design & Optimization:
- Lead architectural decisions for hybrid ML/engineering solutions (APIs, async messaging, databases).
- Optimize full-stack performance (latency, scalability) and troubleshoot issues.
4. Cross-Functional Collaboration:
- Partner with product, data, and infrastructure teams to align AI solutions with business objectives.
Business Acumen & Problem-Solving
1. Strategic Thinking:
- Strong business sense and logical reasoning; ability to synthesize information and generalize patterns.
2. Execution & Ownership:
- Work independently to deliver projects efficiently, focusing on business objectives with speed and quality.
3. Communication & Collaboration:
- Build relationships with key stakeholders; collaborate effectively with remote teams.
- Strong presentation skills, capable of communicating complex ideas in group settings.
Tech Stack & Expertise:
1. ML Engineering: Python, PyTorch/TensorFlow, ONNX, NLP/LLMs, AutoML.
2. LLM/NLP Techniques: Prompt Engineering, RAG optimization, LLM fine-tuning (SFT), Agentic RL optimization.
3. Backend & APIs (Optional): Java/Spring (or Node.js), REST/gRPC, Kafka, SQL/NoSQL.
4. Frontend (Optional): React/Angular, API integrations, visualization tools.
