Job Description Our client is hiring two GenAI Engineers to design and implement generative AI solutions that enhance their Salesforce Knowledge Base and support a large-scale contact center operation. This role focuses on delivering fast, reliable access to information for 700–800 contact center agents who rely on accurate, real-time knowledge while assisting customers on live calls. The organization operates in a fast-paced, KPI-driven environment, where speed to information and quality of responses directly impact customer satisfaction and operational performance. In this role, you will design, build, and operate production-grade retrieval-augmented generation (RAG) systems and agentic AI solutions on AWS, enabling a conversational AI layer on top of the existing Salesforce Knowledge Base. You will support the full lifecycle of AI-powered knowledge delivery—from ingesting newly created Salesforce articles to ensuring agents receive trusted, citation-backed responses during customer interactions. We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to HR@insightglobal.com.To learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/. Skills and Requirements 7+ years software engineering experience (or equivalent depth) Strong Python engineering background with demonstrated experience building and operating cloud-native, production systems. Strong LLM experience must be knowledgeable in how to compare & use best LLM & present solutions and to make the right prompt decisions Extensive hands-on AWS experience, including: Amazon Bedrock (foundation models, embeddings) and Bedrock Guardrails AWS Lambda, CloudWatch, and DynamoDB (schema design and access patterns) Amazon S3 (bucket architecture, automation, and event-driven ingestion) Amazon Kendra (enterprise search and hybrid retrieval use cases) AWS AgentCore / Bedrock Agents for agent orchestration and tool execution Proven expertise in: Large Language Models (prompting patterns, context management, hallucination mitigation) Retrieval-Augmented Generation (chunking strategies, metadata design, hybrid retrieval, reranking, and citations) Data engineering on AWS, including pipelines, data quality checks, lineage, and versioning Database schema awareness and scalable data access patterns for retrieval-heavy workloads Strong production delivery skills, including experience with: API Gateway, Step Functions, EventBridge, SQS/SNS OpenSearch and/or vector database design patterns Security best practices (IAM least privilege, KMS, Secrets Manager/SSM, CloudTrail) CI/CD pipelines and Infrastructure as Code (Terraform, CloudFormation, or CDK) Reliability patterns such as rate limiting, caching, retries, circuit breakers, and disaster recovery awareness AI certification required (AWS ML Specialty or equivalent recognized AI/ML certification), with deep practical knowledge across AWS services.