TECH LEAD — AI ENGINEERING

We are looking for a visionary Technical Lead to head our AI Engineering function — an experienced technologist who has not only built production-grade software systems but has driven the design and delivery of intelligent, agentic AI solutions at scale, with demonstrable experience in LLM-powered and agentic-based solution implementation. You will own the technical direction, mentor a high-performing cross-functional team, and work directly with product and business leadership to translate strategy into working AI-powered products.

Key Responsibilities

AI Strategy & Architecture

▸ Define and own the end-to-end technical architecture for AI agent systems, including multi-agent orchestration, tool-calling pipelines, and agentic workflows.

▸ Lead architectural decisions across RAG (Retrieval-Augmented Generation) systems — from embedding strategies and vector store selection to retrieval optimisation and re-ranking.

▸ Evaluate and adopt frontier LLMs (GPT-4o, Claude, Gemini, Llama, Mistral) and recommend the right model for each use case considering cost, latency, and accuracy.

▸ Drive AI infrastructure decisions: inference serving, fine-tuning pipelines, prompt versioning, and model observability.

Technical Leadership & Delivery

▸ Lead, mentor, and grow a team of AI engineers, ML engineers, prompt engineers, and backend developers.

▸ Set engineering standards — code quality, testing frameworks, CI/CD, and AI-specific evaluation methodologies (LLM evals, human feedback loops).

▸ Own sprint planning, technical roadmaps, and delivery timelines for AI product features and platform initiatives.

▸ Partner with Product Managers, Data Scientists, and QA teams to translate business requirements into executable technical plans.

▸ Represent the AI engineering team in senior stakeholder reviews, CTO/CPO briefings, and client-facing engagements.

AI Agent & LLM Engineering

▸ Design and build autonomous and semi-autonomous AI agents capable of multi-step reasoning, tool use, and memory management.

▸ Implement agentic frameworks (LangGraph, AutoGen, CrewAI, LangChain Agents) for complex workflows including dispute resolution, data processing, and customer-facing automation.

▸ Build and optimise RAG pipelines: document ingestion, chunking strategies, hybrid search (dense + sparse), and context injection.

▸ Develop robust prompt engineering practices — system prompts, few-shot templates, chain-of-thought strategies, and structured output schemas.

▸ Drive responsible AI practices: hallucination mitigation, guardrails, safety layers, and output validation.

Platform Engineering & MLOps

▸ Build scalable AI infrastructure on cloud platforms (AWS / Azure / GCP) — including model hosting, API gateways, and vector databases (Pinecone, Weaviate, pgvector, Qdrant).

▸ Establish monitoring and observability for LLM applications: token usage, latency, drift detection, and feedback integration.

▸ Integrate AI systems with enterprise data sources, REST APIs, and third-party platforms including payment and fintech ecosystems.

Required Qualifications

Experience & Education

▸ 10+ years of overall software engineering experience, with a strong foundation in backend systems, distributed architecture, and API design.

▸ 5+ years of hands-on experience designing, building, and deploying AI agent systems and LLM-powered applications in production.

▸ Proven track record of technical leadership — leading teams of 5+ engineers and delivering complex programmes end-to-end.

▸ Bachelor's or Master's degree in Computer Science, AI/ML, Software Engineering, or a related field (or equivalent demonstrable expertise).

▸ Agentic coding tools

Agentic Coding Tools Experience

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

▸ Hands-on proficiency with LLM APIs: 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 skills — 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, and IaC (Terraform / CDK).

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

Preferred Qualifications

▸ Experience in fintech, payments, or regulated industry environments — understanding of compliance, data residency, and audit requirements.

▸ Exposure to fine-tuning and RLHF workflows using frameworks such as LoRA, QLoRA, or PEFT.

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

▸ Contributions to open-source AI projects or published papers / technical blogs in the AI/ML domain.

▸ Experience building AI products with real-time multi-modal inputs (text, documents, structured data).

▸ Background in building internal AI platforms or tooling for prompt management, model routing, and cost governance.

LEADERSHIP COMPETENCIES

🧭 Strategic Thinking

Connects technical decisions to business outcomes and long-term product vision.

👥 Team Builder

Recruits, develops, and retains top AI engineering talent with a coaching mindset.

⚡ Delivery Excellence

Brings structure and rigour without sacrificing speed; knows when to move fast.

🗣️ Executive Communication

Articulates complex AI concepts clearly to C-suite, clients, and non-technical audiences.

🔬 Intellectual Curiosity

Stays ahead of the AI curve — evaluates new models, techniques, and tooling continuously.

🛡️ Responsible AI Advocate

Champions ethical AI, data privacy, and safety guardrails as core engineering principles.

Skills: ai architecture,ai agent,ml,agentic ai,ai strategy,python,cloud,llm engineering