AI Engineer; Advanced

Position

1972 AI Engineer (Advanced)

Location

Menlyn

Essential Skills

• Large Language Models (LLMs), RAG systems, multi-agent frameworks (Lang Chain, Lang Graph, GAIA)
• Python (Advanced), ML engineering (Tensor Flow, PyTorch, Scikit-learn)
• Vector Databases (FAISS, Pinecone, Elasticsearch/Open Search)
• Prompt Engineering & Context Orchestration
• MLOps (Sage Maker, ML Pipelines, Git Hub Actions, CI/CD)
• AWS AI/ML stack:
  • Sage Maker, Lambda, Step Functions, API Gateway, Dynamo DB, S3
• Data Engineering:
  • Spark, Kafka, SQL/No SQL, ETL/ELT
• Docker, Kubernetes (EKS), Terraform
• Cloud Observability:
  • Cloud Watch, X-Ray, logging/monitoring
• API & Microservices development
• 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, Cloud Formation

Advantageous Skills

• 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

Role & Responsibilities

AI Engineers are responsible for designing, building, and operationalising AI systems that scale across platforms. Responsibilities include:

• 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 (Sage Maker, Lambda, EMR, Step Functions)
• Integrate AI solutions into (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

Qualifications/Experience

• Relevant IT / Engineering / Computer Science / Data Science Degree
• Preferred Certifications:
  • AWS ML Specialty
  • AWS Solutions Architect (Associate/Professional)
  • Deep Learning. AI (GenAI, LLMs, Agents)
  • Hashicorp Terraform Associate

Submit your CV to: [email protected] and Subject line: 1972 AI Engineer (Advanced)