01Overview
You should have :
- 5+ years of professional experience in software engineering.
- Approximately 3+ years of experience working with Machine Learning, NLP, or related AI technologies.
- At least 1.5+ years of hands-on experience building LLM or Generative AI applications.
- Strong, production-grade programming skills in Python.
- Experience designing and implementing scalable software systems.
- A strong understanding of modern LLM and Generative AI application architecture.
- Experience building or contributing to production-grade RAG systems.
- The ability to take ownership of technical solutions and contribute to architectural decisions.
Technical Skills :
Generative AI & LLMs :
- Hands-on experience with one or more of the following :
1. GPT
2. Claude
3. Gemini
4. Llama
5. Other Large Language Models
- Experience with :
1. Prompt engineering
2. Context engineering
3. Embeddings
4. Inference optimisation
5. LLM application development
6. Evaluation and response quality
RAG & Search :
- Experience with :
1. Retrieval-Augmented Generation
2. Document ingestion and preprocessing
3. Chunking strategies
4. Vector search
5. Semantic retrieval
6. Response relevance and evaluation
What Will Make You Stand Out :
- We would be particularly interested in candidates with experience in:
1. Building agentic AI or autonomous workflows.
2. Tool-based reasoning and AI orchestration.
3. LangChain, Google ADK, LangGraph, or similar frameworks.
4. PyTorch or TensorFlow.
5. MLOps platforms and practices.
6. Kubernetes and cloud-native deployment.
7. Production AI monitoring and evaluation.
8. Enterprise search or large-scale document processing.
9. Financial Services, Banking, BFSI, or Capital Markets.
10. Working within large, complex, or highly regulated enterprise environments.
You will be working at the intersection of :
- Generative AI + RAG + NLP + Agentic AI + Software Engineering
You will have the opportunity to :
- Build AI applications that solve real business problems.
- Work with some of the most advanced LLM technologies available.
- Design systems capable of searching and understanding massive enterprise knowledge bases.
- Gain hands-on exposure to emerging agent-based architectures.
- Work on challenging problems involving retrieval accuracy, scale, relevance, and performance.
- Build reusable AI services and production-ready applications.
- Collaborate with experienced engineers and AI specialists.
- Help shape the future of AI adoption within a global financial services organisation. .
