1972 AI Engineer (Advanced)

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
• 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

POSITION INFO:
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 (SageMaker, 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

• 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