What are the responsibilities and job description for the AI Platform Engineer position at PineQ Lab Technology?
" 6 years of software, cloud, data, or platform engineering experience, including 3 years designing AI/ML or Generative AI solutions on AWS.
Hands-on experience building agents with the AWS Strands Agents SDK and deploying them through Amazon Bedrock AgentCore.
Architecture-level knowledge of RAG, Bedrock Knowledge Bases, chunking, embedding models, vector stores, semantic retrieval, metadata filtering, and evaluation.
Experience building scalable ingestion, embedding, indexing, reprocessing, and data-quality pipelines using AWS Lambda and Glue.
Practical experience configuring and tuning HNSW, IVF, or comparable approximate nearest-neighbor indexes.
Experience integrating AI and retrieval evaluation, regression checks, safety tests, and quality gates into CI/CD.
Proficiency in prompt engineering, testing, versioning, and shared prompt-library design.
Strong Python and JavaScript skills, including API integration, automated testing, packaging, debugging, and maintainable design.
Knowledge of AWS IAM, networking, encryption, secrets, monitoring, least-privilege security, architecture documentation, and production runbooks.
Preferred Qualifications
Experience with OpenSearch, pgvector, Pinecone, Redis, or comparable vector technologies, plus hybrid search, reranking, and advanced RAG patterns.
Experience with infrastructure as code, containers, cloud-native CI/CD, and reusable APIs, SDKs, or engineering templates.
Knowledge of LLM observability, hallucination analysis, prompt-injection defenses, guardrails, and responsible AI controls.
Bachelor s degree in Computer Science, Engineering, Information Technology, or a related field, or equivalent practical experience."
Hands-on experience building agents with the AWS Strands Agents SDK and deploying them through Amazon Bedrock AgentCore.
Architecture-level knowledge of RAG, Bedrock Knowledge Bases, chunking, embedding models, vector stores, semantic retrieval, metadata filtering, and evaluation.
Experience building scalable ingestion, embedding, indexing, reprocessing, and data-quality pipelines using AWS Lambda and Glue.
Practical experience configuring and tuning HNSW, IVF, or comparable approximate nearest-neighbor indexes.
Experience integrating AI and retrieval evaluation, regression checks, safety tests, and quality gates into CI/CD.
Proficiency in prompt engineering, testing, versioning, and shared prompt-library design.
Strong Python and JavaScript skills, including API integration, automated testing, packaging, debugging, and maintainable design.
Knowledge of AWS IAM, networking, encryption, secrets, monitoring, least-privilege security, architecture documentation, and production runbooks.
Preferred Qualifications
Experience with OpenSearch, pgvector, Pinecone, Redis, or comparable vector technologies, plus hybrid search, reranking, and advanced RAG patterns.
Experience with infrastructure as code, containers, cloud-native CI/CD, and reusable APIs, SDKs, or engineering templates.
Knowledge of LLM observability, hallucination analysis, prompt-injection defenses, guardrails, and responsible AI controls.
Bachelor s degree in Computer Science, Engineering, Information Technology, or a related field, or equivalent practical experience."