Senior Software Engineer - AI
Role Overview
We are looking for a skilled AI Senior Software Engineer to design and build Generative AI applications that solve real business problems.
This is a Gen AI–first role: your primary work will be developing LLM-powered systems using frameworks like LangChain, LangGraph, and Hugging Face, with RAG pipelines, vector search, and prompt engineering at the core of what you do.
Our architecture separates AI services from the user-facing application.
Your primary focus is owning the AI services end-to-end — from model integration and inference pipelines through to production deployment.
This split lets you go deep on AI while still maintaining visibility into how your work ships to customers.
You will collaborate with product managers, engineers, and business stakeholders to deliver AI features that create measurable value.
Job requirements
• 3–8 years of professional software development experience, with at least 1–2 years focused on Generative AI or LLM application development.
• Hands-on production experience building LLM-powered applications using LangChain and/or LangGraph.
• Practical experience with Hugging Face — using pre-trained models, the Transformers library, or the Inference API.
• Working knowledge of RAG architectures, vector databases (e.g. Pinecone, Weaviate, pgvector, or MongoDB Atlas Vector Search), and embedding techniques.
• Strong Python skills — writing clean, well-tested, and maintainable code, ideally with experience in FastAPI or similar API frameworks.
• Working proficiency in the MEAN stack: MongoDB, Express.js, Angular (17+), and Node.js.
• Experience designing and consuming REST APIs; familiarity with GraphQL is expected for this role.
• Comfortable using Git and working within modern CI/CD workflows.
• Clear communicator who works effectively with both technical and non-technical stakeholders.
Nice to Have
• Experience deploying AI-powered applications on any one of the cloud providers: AWS, Azure, or GCP.
• Exposure to LLM evaluation frameworks, observability tools (e.g. LangSmith, Langfuse), or fine-tuning workflows.
• Prior experience integrating third-party AI APIs (OpenAI, Anthropic, Bedrock, Cohere) in production.
• Familiarity with MongoDB Atlas Vector Search or other native vector capabilities in operational databases.
• Experience in an agile team environment with iterative product delivery.
Job responsibilities
Key Skills for This Role
Full Job Posting
Job requirements
- 3–8 years of professional software development experience, with at least 1–2 years focused on Generative AI or LLM application development.
- Hands-on production experience building LLM-powered applications using LangChain and/or LangGraph.
- Practical experience with Hugging Face — using pre-trained models, the Transformers library, or the Inference API.
- Working knowledge of RAG architectures, vector databases (e.g. Pinecone, Weaviate, pgvector, or MongoDB Atlas Vector Search), and embedding techniques.
- Strong Python skills — writing clean, well-tested, and maintainable code, ideally with experience in FastAPI or similar API frameworks.
- Working proficiency in the MEAN stack: MongoDB, Express.js, Angular (17+), and Node.js.
- Experience designing and consuming REST APIs; familiarity with GraphQL is expected for this role.
- Comfortable using Git and working within modern CI/CD workflows.
- Clear communicator who works effectively with both technical and non-technical stakeholders.
- Experience deploying AI-powered applications on any one of the cloud providers: AWS, Azure, or GCP.
- Exposure to LLM evaluation frameworks, observability tools (e.g. LangSmith, Langfuse), or fine-tuning workflows.
- Prior experience integrating third-party AI APIs (OpenAI, Anthropic, Bedrock, Cohere) in production.
- Familiarity with MongoDB Atlas Vector Search or other native vector capabilities in operational databases.
- Experience in an agile team environment with iterative product delivery.
Job responsibilities
- Design, develop, and maintain LLM-powered application features using Python and frameworks such as LangChain and LangGraph.
- Build and maintain Retrieval-Augmented Generation (RAG) pipelines — including chunking, embedding, retrieval, and re-ranking strategies.
- Integrate Hugging Face models and APIs for text generation, classification, embeddings, and semantic understanding tasks.
- Develop and tune prompt engineering strategies, manage LLM context, and ensure reliable, production-grade outputs.
- Build agentic workflows using LangGraph — orchestrating tool use, multi-step reasoning, and stateful agent behaviour.
- Expose AI capabilities through clean Python APIs (e.g. FastAPI) that the MEAN application can consume.
- Apply responsible AI principles, ensuring solutions are fair, explainable, and aligned with organisational and regulatory standards.
- Evaluate and recommend emerging Generative AI tools, models, and frameworks that add tangible value to the product.
- Contribute to the MEAN application (MongoDB, Express.js, Angular, Node.js), with a focus on integrating AI services into the product experience.
- You will also collaborate on the user-facing application — touching Node.js APIs, MongoDB, and Angular where needed to integrate AI capabilities — but the depth of ownership sits with the Python AI services.
- Participate in code reviews to uphold quality and share knowledge across the team.
- Troubleshoot and debug issues across the stack — from LLM response quality and retrieval accuracy to API performance and UI integration.
- Contribute clear technical documentation for AI services, APIs, and integration points.
- Work within agile/scrum delivery practices, collaborating closely with product managers and fellow engineers.
Job benefits
- A collaborative engineering team working on meaningful, real-world Generative AI problems.
- Opportunities to grow into senior and leadership roles as the team scales.
- Access to cutting-edge Gen AI tools, models, and a culture of continuous learning.
- Flexible working arrangements and a commitment to work-life balance.
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