Senior AI Engineer (GenAI and Data Platform - AWS)

• 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.

Note: Must Have Skills:

• Generative AI / LLM (RAG, embeddings, prompt engineering)
• AWS Cloud (OpenSearch, Neptune, DynamoDB, ElastiCache/Redis)
• Vector Search & Retrieval Systems (OpenSearch / vector DB)
• Graph Databases (Amazon Neptune, knowledge graphs)
• LLM Frameworks (LangChain / LlamaIndex)
• Agentic AI Frameworks (LangGraph / AutoGen / CrewAI)
• Databricks & Apache Spark (data pipelines, embedding pipelines)

• Backend/API Development (Python, scalable APIs, microservices), 1. 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 2. 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

1. 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.

2. 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

3. 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

4. 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

• AWS Certified Solutions Architect OR
• AWS Certified Machine Learning Specialty OR
• AWS Data Engineer Certification

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, * 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, * 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.