AI Engineer (Entry)

Imizizi

Large Language Models (LLMs), RAG systems, multi agent frameworks (LangChain, LangGraph, GAIA)
Python (Advanced), ML engineering (TensorFlow, PyTorch, Scikit learn)
Vector Databases (FAISS, Pinecone, Elasticsearch/OpenSearch)
Prompt Engineering & Context Orchestration
MLOps (SageMaker, ML Pipelines, GitHub Actions, CI/CD)
AWS AI/ML stack: SageMaker, Lambda, Step Functions, API Gateway, DynamoDB, S3
Data Engineering: Spark, Kafka, SQL/NoSQL, ETL/ELT
Docker, Kubernetes (EKS), Terraform
Cloud Observability: CloudWatch, X Ray, logging/monitoring
API & Microservices development
Cloud Data Hub (CDH), CDEC Integration
Experience with Confluence, JIRA, and creating technical documentation
Understanding of data governance, AI risk, security, compliance, and IAM
Basic experience/understanding of AWS Components (in order of importance):
Glue, Kinesis (Streams & Firehose), Redshift, Athena, SNS, SQS, Secrets Manager, Param Store, CloudFormation.

Experience building production grade AI systems (RAG, agents, embeddings, search)
Strong mathematical foundations in probability, optimization & deep learning
AI cost optimization and performance tuning
Experience with graph based reasoning, semantic caching, and model evaluation strategies
Strong communication skills for translating business needs into AI architectures
Ability to design AI safety controls and ensure model reliability
Experience preparing architectural specifications, solution designs and PoC to production handover
Strong organisational skills and ability to lead technical streams

AI Engineers are responsible for designing, building, and operationalising AI systems that scale across clients platforms.
Build enterprise grade AI/ML systems (LLMs, RAG, multi agent automation, vision/NLP models)
Develop retrieval pipelines, embedding strategies, semantic indexes & vector search
Build and maintain cloud native AI workflows using AWS (SageMaker, Lambda, EMR, Step Functions)
Integrate AI solutions into clients' platforms (CDEC, CDH, SAP, microservices)
Lead PoCs, architecture designs, and collaborate with global stakeholders
Implement MLOps pipelines & model monitoring (drift detection, auto retraining)
Ensure security alignment, model governance, responsible AI practices
Document designs, decisions, evaluations, and solution patterns
Support Agile ceremonies, sprint planning, backlog refinement and cross team collaboration

Submit your CV to: ***email_hidden*** and Subject line