Senior AI Engineer (GenAI + Data Platform – AWS)

Saransh Inc

Job Location:

Irvine, CA - USA

Monthly Salary: Not Disclosed

Posted on: 6 hours ago

Vacancies: 1 Vacancy

Job Summary

Role: Senior AI Engineer (GenAI Data Platform AWS)

Location: 4 days a week onsite is must (3 days in Irvine CA & 1 Day in Downtown LA CA)

Job Type: Contract

Role Summary:

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)

Must Have Certifications:
AWS Certification (Preferred):

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

Key Responsibilities:
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

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:

Bachelors or Masters 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.

Role: Senior AI Engineer (GenAI Data Platform AWS) Location: 4 days a week onsite is must (3 days in Irvine CA & 1 Day in Downtown LA CA) Job Type: Contract Role Summary: We are seeking a Senior AI Engineer to design build and scale a production-grade Generative AI and Data Platform on AWS....