Senior GenAI Application Engineer

Experience: 6+ Years
Role: Senior GenAIApplication Engineer

Key Skills:
· Production GenAI Application Engineering

· LangGraph, LangChain, RAG and Agentic Workflows

• Python, Java, APIs and Enterprise Integration

Responsibilities:

We are looking for a Senior GenAI Application Engineer who operates effectively at the intersection of software engineering, GenAI application development, enterprise integration and production delivery.

This is not a pure research role. It is not a prompt engineering only role. The successful candidate will help design, build and improve production grade GenAI applications that are reliable, observable, maintainable and useful to real enterprise users.

The successful candidate will:

1. Build and improve GenAI applications using frameworks such as LangGraph, LangChain or similar orchestration tools.
2. Work with open weight models, hosted LLMs and model serving patterns.
3. Develop agentic workflows, retrieval augmented generation, tool calling and prompt orchestration.
4. Integrate GenAI applications with enterprise systems, APIs, data sources and operational platforms.
5. Engineer solutions for production quality, including logging, tracing, evaluation, fallback behaviour and troubleshooting.
6. Bring a strong can do attitude, enthusiasm, curiosity and practical delivery mindset.

Finance or banking experience is not required. What matters more is hands on experience building real GenAI applications, strong engineering judgement and the ability to deliver in a complex enterprise environment.

What Matters Most

1. Strong hands on experience building production grade GenAI applications.
2. Practical experience with LangGraph, LangChain, agentic workflows, RAG and tool calling.
3. Understanding of open weight models and how to integrate them into real applications.
4. Ability to challenge weak designs and propose better ones.
5. Strong engineering discipline around reliability, testing, observability and maintainability.
6. Ability to work across application, data, platform, security and infrastructure teams.
7. Enthusiastic, energetic and willing to get into the details.
8. Pragmatic approach to delivery, not over engineering, not hype driven.

Requirements:

Below are the key skillsets required for the role:

1. 6 or more years of software engineering experience, preferably with recent hands on experience in GenAI application development.
2. Proven experience building production grade GenAI applications, not only prototypes, experiments or demos.
3. Strong hands on experience with LangGraph, LangChain or similar orchestration frameworks.
4. Practical understanding of RAG, agentic workflows, tool calling, prompt orchestration and context management.
5. Experience integrating LLMs into real applications using APIs, backend services and enterprise data sources.
6. Experience with open weight models or strong interest backed by hands on experimentation.
7. Strong backend engineering skills using Python, Java or similar languages.
8. Good understanding of API design, distributed systems and production resilience patterns.
9. Experience with observability, logging, tracing and troubleshooting for GenAI or backend applications.
10. Familiarity with containerised deployment environments such as Kubernetes or OpenShift.
11. Ability to write clean, maintainable and testable code.
12. Strong analytical, debugging and troubleshooting skills.
13. High ownership, high curiosity and genuine interest in building useful AI products.
14. Ability to work in a fast moving environment with incomplete information and evolving requirements.
15. Strong communication skills, with the ability to challenge weak designs and coordinate across business, application, data, infrastructure and security teams.

Nice to Have

1. Experience with DeepAgent or similar agent frameworks.
2. Experience with Langfuse, Elastic or similar observability and search tools.
3. Experience with Redis for caching, conversation state, rate limiting, queue backed workflows or low latency GenAI application patterns.
4. Experience with vLLM or similar inference serving frameworks for running open weight models.
5. Experience deploying GenAI workloads on cloud or container platforms.