AI Engineer
Posted Updated
Summary
AI Engineer in Abuja building and owning production LLM-powered systems end-to-end — agents, RAG, context engineering, AI evaluation frameworks, and model fine-tuning/deployment — using Python, FastAPI, PyTorch, Hugging Face, and inference frameworks like vLLM, with focus on security, cost, and reliability.
- Build and own production AI
systems end-to-end, including LLM-powered
applications, intelligent agents, conversational AI, document processing,
decision-support tools, and automation workflows, from architecture and
prototyping through deployment, monitoring, and continuous improvement.
- Design agent, RAG, and
context-engineering systems, including
retrieval, embeddings, reranking, grounding, memory, compaction, caching,
token management, tool/function calling, agent harnesses, retries, state
management, and failure recovery.
- Build robust AI evaluation and
quality frameworks to measure accuracy,
relevance, hallucination, safety, retrieval quality, latency, cost, and
task completion, using regression testing, human evaluation, LLM-as-judge,
and production feedback to continuously improve system performance.
- Evaluate, train, deploy, and
optimise AI models, selecting between frontier
and open-weight models and applying techniques such as SFT, LoRA/QLoRA,
preference tuning, quantisation, and self-hosted inference where
appropriate, while optimising for capability, security, privacy, latency,
and cost.
- Engineer reliable and secure
production AI infrastructure, developing
Python/FastAPI services, APIs, data and inference pipelines, and observability
while collaborating with Product, Software, and Platform Engineering teams
and maintaining appropriate technical documentation, security controls,
and responsible AI practices.
Requirements
- 3+ years of relevant experience in AI Engineering, Machine Learning Engineering, Applied AI,
Data Science, or related roles, with demonstrable experience designing,
shipping, and maintaining LLM-powered systems in production.
- Strong proficiency in Python,
FastAPI, APIs, SQL/databases, Git, Docker, and Linux, with solid
software engineering fundamentals including asynchronous systems, retries,
state management, testing, observability, debugging, scalability, latency,
and cost optimisation.
- Hands-on experience with LLMs,
Generative AI, RAG, agents, and context engineering, including prompt
engineering, embeddings, vector/hybrid search, retrieval, reranking,
tool/function calling, memory, context compaction, caching, grounding, and
token management.
- Demonstrable experience building AI
evaluation and model engineering pipelines, including regression
testing, LLM-as-judge/human evaluation, hallucination mitigation, model
benchmarking, and working with open-weight models through techniques such
as SFT, LoRA/QLoRA, DPO/preference tuning, and production inference.
- Experience with relevant AI/ML
and production technologies such as PyTorch, Hugging Face, vector
databases, commercial and self-hosted LLMs, and inference frameworks such
as vLLM/SGLang, with a strong understanding of AI security, privacy,
responsible AI, model selection, performance, and production reliability.
Benefits
- Opportunity to build and shape production
AI products and AI engineering standards.
- Access to challenging real-world
problems and opportunities to take solutions from experimentation to
production.
- Professional development and
certification support.
- Competitive salary and benefits.