We are seeking a Senior AI Engineer to design, build, and scale a production-grade Generative AI and Data Platform on AWS. The role focuses on enabling LLM-powered capabilities through vector search, graph-based knowledge systems, and governed data pipelines. The ideal candidate will own end-to-end delivery across the AI lifecycle, including: Data ingestion and knowledge curation Embeddings and retrieval systems Backend services and APIs CI/CD pipelines and deployment This role will closely partner with product and engineering teams to operationalize AI capabilities in externally facing applications and drive evolution toward agentic AI systems.
Key Responsibilities
GenAI Enablement & Integration
Build and operationalize LLM-powered applications using: Retrieval-Augmented Generation (RAG) Embeddings pipelines Prompt orchestration and evaluation frameworks Design and implement vector search systems using Amazon OpenSearch Develop graph-based knowledge systems using Amazon Neptune for relationships, lineage, and explainability Integrate supporting infrastructure: Amazon ElastiCache (Redis) for session state and caching DynamoDB for scalable, low-latency data access Implement agentic workflows using frameworks such as: LangGraph, AutoGen, CrewAI (or equivalent) Integrate with LLM frameworks like: LangChain, LlamaIndex (tool calling, retrieval orchestration, context management) Define standards for: Tool integration Context-sharing patterns (MCP-style designs) Evaluate LLM models and retrieval strategies across: Latency Cost Accuracy Context limitations
Data Pipelines & Knowledge Engineering
Design and build scalable data pipelines using Databricks and Apache Spark Implement: Data ingestion and transformation pipelines Document processing (chunking, metadata tagging) Embedding generation and indexing Ensure high data quality standards: Validation, completeness, consistency, monitoring Implement data governance frameworks: Data classification and access controls Retention policies Auditability and lineage tracking
Backend Services & APIs
Develop backend services exposing AI capabilities through secure and scalable APIs Define best practices for: API contracts and versioning Reliability (retry logic, circuit breakers, idempotency) Enable reusability of platform capabilities across teams and applications
Deployment, MLOps & Operational Excellence
Build and manage CI/CD pipelines for AI and data workloads Deploy production systems using: Docker (containerization) Kubernetes (orchestration) Implement deployment strategies: Blue/green deployments Canary releases Rollback strategies Feature flags Ensure system reliability through: Monitoring (latency, failures, cost, data freshness) Alerting and observability Secrets management and least-privilege access Optimize platform performance and cost
LLM Observability, Evaluation & Quality
Define and track GenAI quality metrics: Grounding / faithfulness Retrieval relevance Response consistency Latency and cost per request Implement: Prompt/version tracking Offline evaluation pipelines Continuous improvement workflows
LLM Security, Safety & Compliance
Implement secure AI systems with: Access control and authentication Data protection policies Responsible AI guardrails Ensure compliance with best practices in: AI safety Data privacy Monitoring and auditability
Required Skills
Strong experience in Generative AI / LLM systems (RAG, embeddings, prompt engineering)
Hands-on experience with AWS ecosystem
Expertise in: OpenSearch (vector search) Neptune (graph databases) DynamoDB and Redis (ElastiCache)
Experience with: LangChain / llamaIndex Agentic AI frameworks (LangGraph, AutoGen, CrewAI)
Strong programming skills (Python preferred)
Experience with Databricks and Apache Spark
Solid understanding of: Data pipelines Distributed systems API design
Preferred Skills
Experience with: Model evaluation frameworks and LLM observability tools AI governance and compliance frameworks Kubernetes and advanced MLOps practices Familiarity with: Model Context Protocol (MCP) patterns Agent-based architectures
Qualifications
Bachelor's or Master's degree in: Computer Science / Data Science / AI / related field
Proven experience building production-grade AI platforms and systems
Strong background in end-to-end AI/ML lifecycle delivery
Soft Skills
Strong problem-solving and analytical thinking
Ability to communicate complex AI concepts clearly
Collaborative and cross-functional mindset
Ownership-driven and proactive execution