Senior AI Engineer - GenAI + Data Platform - AWS

Compunnel, Inc.; Los Angeles (CA); On-site; USD 120,000 - 160,000; Full time

Job summary

Compunnel, Inc. is seeking a Senior AI Engineer to design and build a production-grade Generative AI and Data Platform on AWS. This role will focus on enabling LLM-powered capabilities, integrating data pipelines, and operationalizing AI systems.

The ideal candidate will have strong experience in Generative AI, AWS ecosystem, and solid programming skills in Python. Responsibilities include building scalable applications, developing backend services, and ensuring system reliability through best practices in MLOps.

Qualifications

• Strong experience in Generative AI / LLM systems.
• Hands-on experience with the AWS ecosystem.
• Expertise in Amazon OpenSearch, DynamoDB, and Redis.
• Experience with LangChain / LlamaIndex frameworks.
• Strong programming skills, preferably in Python.
• Experience with Databricks and Apache Spark.
• Solid understanding of data pipelines and API design.
• Proven experience building production-grade AI platforms.

Responsibilities

• Build and operationalize LLM-powered applications.
• Design and implement scalable data pipelines.
• Develop backend services with secure APIs.
• Build and manage CI/CD pipelines for AI workflows.
• Implement secure AI systems with compliance measures.

Skills

Generative AI / LLM systems

AWS ecosystem

Amazon OpenSearch

Amazon Neptune

DynamoDB

Redis (ElastiCache)

LangChain

LlamaIndex

LangGraph

AutoGen

CrewAI

Python

Databricks

Apache Spark

Education

Bachelor’s or Master’s degree in Computer Science

Tools

Docker

Kubernetes

Job description

Job Description

Senior AI Engineer responsible for designing, building, and scaling a production‑grade Generative AI and Data Platform on AWS. This role focuses on enabling LLM‑powered capabilities through vector search, graph‑based knowledge systems, and governed data pipelines. The engineer will own end‑to‑end delivery across the AI lifecycle and partner with product and engineering teams to operationalize AI capabilities and drive evolution toward agentic AI systems.

Key Responsibilities

• GenAI Enablement & Integration: Build and operationalize LLM‑powered applications using RAG, embeddings pipelines, and prompt orchestration. Design and implement vector search systems with Amazon OpenSearch, graph‑based knowledge systems with Amazon Neptune, and integrate supporting infrastructure such as Amazon ElastiCache and DynamoDB. Implement agentic workflows using frameworks such as LangGraph, AutoGen, or CrewAI, and integrate with LLM frameworks like LangChain or LlamaIndex. Define standards for tool integration and context‑sharing patterns. Evaluate LLM models and retrieval strategies across latency, cost, accuracy, and context limitations.
• Data Pipelines & Knowledge Engineering: Design and build scalable data pipelines using Databricks and Apache Spark, including data ingestion, transformation, and document processing. Implement embedding generation and indexing. Ensure high data quality standards and implement data governance frameworks.
• Backend Services & APIs: Develop backend services exposing AI capabilities through secure and scalable APIs. Define best practices for API contracts, versioning, and reliability. Enable reusability of platform capabilities.
• Deployment, MLOps & Operational Excellence: Build and manage CI/CD pipelines for AI and data workloads. Deploy production systems using Docker and Kubernetes, implementing strategies such as blue/green deployments and canary releases. Ensure system reliability through monitoring, alerting, and observability. Optimize platform performance and cost.
• LLM Observability, Evaluation & Quality: Define and track GenAI quality metrics. Implement prompt and version tracking, offline evaluation pipelines, and continuous improvement workflows.
• LLM Security, Safety & Compliance: Implement secure AI systems with access control, authentication, and data protection policies. Implement responsible AI guardrails and ensure compliance with best practices in AI safety and data privacy.

Required Qualifications

• Strong experience in Generative AI / LLM systems (RAG, embeddings, prompt engineering).
• Hands‑on experience with the AWS ecosystem.
• Expertise in Amazon OpenSearch, Amazon Neptune, DynamoDB, and Redis (ElastiCache).
• Experience with LangChain / LlamaIndex and agentic AI frameworks such as LangGraph, AutoGen, or CrewAI.
• Strong programming skills, preferably in Python.
• Experience with Databricks and Apache Spark.
• Solid understanding of data pipelines, distributed systems, and API design.
• Bachelor’s or Master’s degree in Computer Science, Data Science, AI, or a related field.
• Proven experience building production‑grade AI platforms and systems.
• Strong background in end‑to‑end AI/ML lifecycle delivery.
• Strong problem‑solving and analytical thinking skills.
• Ability to communicate complex AI concepts clearly.
• Collaborative and cross‑functional mindset.
• Ownership‑driven and proactive execution.

Preferred Qualifications

• Experience with model evaluation frameworks and LLM observability tools.
• Experience with AI governance and compliance frameworks.
• Experience with Kubernetes and advanced MLOps practices.
• Familiarity with Model Context Protocol (MCP) patterns.
• Familiarity with agent‑based architectures.