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# Senior AI Engineer (GenAI and Data Platform - AWS)

- **Company:** Saransh Inc
- **Location:** Irvine, CA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Automated Storage and Retrieval Systems, Audit Trail, Encodings, Computer Programming, Data Governance, Data Infrastructure, Data Security, Distributed Systems, Amazon DynamoDB, Graph Database, Python (Programming Language), Key Management, Knowledge-Based Systems, Machine Learning, Redis, Software Safety, Search Technologies, Management of Software Versions, Data Classification, Data Ingestion, Amazon ElastiCache, Evaluation Pipelines, Retrieval-Augmented Generation, Large Language Models, Grafana, Prompt Engineering, Apache Spark, Model Validation, Llamaindex, Caching, Reliability of Systems, Generative AI, Backend, Agentic-AI, Knowledge Engineering, Build Management, Kubernetes, Information Technology, Data Lineage, Low Latency, Deployment Automation, CrewAI, AutoGen, AWS Data Analytics, Machine Learning Operations, Api Design, Restful APIs, Model Context Protocol, Prompt Orchestration, Amazon OpenSearch, GPT, Data Pipelines, Api Management, OpenSearch, Docker, Databricks, Microservices
- **Published:** November 4, 2026
- **Apply:** https://www.thejobnetwork.com/job/f16efc05-1b09-4971-9f02-d1af4cd90b86/senior-ai-engineer-genai-and-data-platform-aws

## About the Role

* 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.

## Description

* 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

 3. 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.

 4. 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

 5. 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

 6. 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