Job Opportunities

SENIOR AI DEVELOPER

We are seeking a Senior AI Developer who thrives at the intersection of software engineering and applied AI. You will design, build, and optimise LLM-powered features, AI agent pipelines, and RAG systems that are reliable, scalable, and production-ready. With 8+ years of engineering experience and 3+ years focused on AI agent and LLM development, you bring both depth and versatility — moving fluidly between system design, hands-on coding, and cross-team collaboration.


KEY RESPONSIBILITIES

AI Feature Development

▸   Design, develop, and ship LLM-powered features including conversational agents, document intelligence, and automated decision-support tools.

▸   Build and maintain production-grade RAG pipelines — document ingestion, chunking, embedding, vector retrieval, re-ranking, and context injection.

▸   Develop and iterate on prompt engineering artefacts: system prompts, few-shot templates, chain-of-thought strategies, and structured output schemas.

▸   Implement and test AI agent workflows using frameworks such as LangGraph, LangChain, AutoGen, or CrewAI.

Engineering Quality & Delivery

▸   Drive the use of agentic coding tools (Claude Code, Cursor, GitHub Copilot, or equivalent) to automate and accelerate the software delivery lifecycle — translating PRDs and technical specs into working code, conducting AI-assisted code reviews, generating test cases, and enforcing quality criteria across the output.

▸   Expected to define and maintain quality standards for AI-generated code and continuously improve agentic workflows as tooling evolves.

▸   Build LLM evaluation harnesses (Evals, RAGAS, TruLens, PromptFoo) to measure output quality, regression, and safety across model updates.

▸   Implement observability for AI systems: latency tracking, token usage monitoring, drift detection, and user feedback integration.

▸   Participate actively in code reviews, technical design discussions, and sprint planning.

Model & Platform Integration

▸   Integrate with LLM APIs (OpenAI, Anthropic Claude, Google Gemini, Cohere, HuggingFace) and select the right model per use case.

▸   Work with vector databases (Pinecone, Weaviate, pgvector, Qdrant, Chroma) and optimise retrieval performance at scale.

▸   Support fine-tuning workflows using SFT, LoRA, QLoRA, or PEFT where required for domain-specific performance.

▸   Integrate AI components with enterprise APIs, data pipelines, and third-party platforms including fintech and payments ecosystems.

Collaboration & Knowledge Sharing

▸   Collaborate closely with the Tech Lead, Product Managers, QA, and Prompt Engineers to translate requirements into shipped AI features.

▸   Document technical decisions, architecture choices, and model evaluation results clearly for team and stakeholder consumption.

▸   Mentor junior developers on AI best practices, responsible AI principles, and engineering standards.

REQUIRED QUALIFICATIONS

 Experience & Education

▸   8+ years of overall software engineering experience with a strong backend and API design foundation.

▸   3+ years of focused, hands-on experience building and deploying AI agent systems, LLM-powered applications, and RAG pipelines in production.

▸   Demonstrable experience with LLM-based and agentic solution implementation in real-world, at-scale environments.

▸   Bachelor's or master's degree in computer science, AI/ML, Software Engineering, or equivalent practical expertise.

Technical Skills

▸   Deep expertise in RAG architecture: chunking, embeddings, vector search, hybrid retrieval, document parsing, Evals, and SFT (Supervised Fine-Tuning).

▸   LLM API proficiency: OpenAI, Anthropic (Claude), Google Gemini, Cohere, and open-source models via HuggingFace / Ollama.

▸   Agentic frameworks: LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel, or equivalent.

▸   Strong Python engineering — async programming, packaging, testing, and production-grade code standards.

▸   Vector databases: Pinecone, Weaviate, Milvus, pgvector, Qdrant, or Chroma.

▸   Cloud & DevOps: AWS / Azure / GCP, Docker, Kubernetes, CI/CD pipelines.

▸   Data engineering: ETL pipelines, SQL/NoSQL databases, streaming platforms (Kafka / Pub-Sub).

PREFERRED QUALIFICATIONS

 ▸   Experience in fintech, payments, or regulated industries — familiarity with compliance, data residency, and auditability requirements.

▸   Hands-on fine-tuning experience: LoRA, QLoRA, PEFT, or RLHF workflows.

▸   Familiarity with AI evaluation frameworks: RAGAS, TruLens, PromptFoo, or custom eval harnesses.

▸   Exposure to multi-modal AI inputs (text, documents, structured data) in production systems.

▸   Open-source contributions or published technical writing in the AI/ML space.

CORE COMPETENCIES

 🔨  Builder Mindset
Ships clean, well-tested, production-ready AI systems — not just prototypes.

🧠  Deep Technical Ownership
Takes end-to-end ownership of features from architecture through deployment and monitoring.

🔬  Curious Experimenter
Evaluates new models, frameworks, and techniques — and knows when to adopt vs. wait.

🤝  Strong Collaborator
Works closely with PM, QA, and design; communicates technical decisions clearly.

📐  Quality-First
Writes robust, maintainable code with LLM evals, unit tests, and observability built in.

🛡️  Responsible AI
Applies guardrails, safety layers, and hallucination mitigation as default practice.