AI Engineer (Entry)

SUMMARY:
ESSENTIAL SKILLS:

• 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.

POSITION INFO:
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. 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 client 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 QUALIFICATIONS\/EXPERIENCE: Relevant IT \/ Engineering \/ Computer Science \/ Data Science Degree Preferred Certifications: AWS ML Specialty AWS Solutions Architect (Associate\/Professional) DeepLearning.AI (GenAI, LLMs, Agents) Hashicorp Terraform Associate Submit your CV to: and Subject line Role title