AI Engineer

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

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

1. 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

1. 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

1. 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

1. 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

• Skill 1 - Generative AI / LLM (RAG, embeddings, prompt engineering)
• Skill 2 - AWS Cloud (OpenSearch, Neptune, DynamoDB, ElastiCache/Redis)
• Skill 3 - Vector Search & Retrieval Systems (OpenSearch / vector DB)
• Skill 4 - Graph Databases (Amazon Neptune, knowledge graphs)
• Skill 5 - LLM Frameworks (LangChain / LlamaIndex)
• Skill 6 - Agentic AI Frameworks (LangGraph / AutoGen / CrewAI)
• Skill 7 - Databricks & Apache Spark (data pipelines, embedding pipelines)
• Skill 8 - Backend/API Development (Python, scalable APIs, microservices), * AI/ML Platform Engineering
• Generative AI / LLM Applications

• Data Platform / Big Data Engineering ______________ Must Have Certifications -

• AWS Certification (Preferred):
• AWS Certified Solutions Architect OR
• AWS Certified Machine Learning Specialty OR
• AWS Data Engineer Certification, 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

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

Skills: Amazon Web Services (AWS), Apache Spark, Application Programming Interface (API), Artificial Intelligence (AI), Best Practices, Circuit Breakers, Cloud Computing, Communication Skills, Computer Programming, Computer Science, Continuous Deployment/Delivery, Continuous Improvement, Continuous Integration, Create Graphs, Cross-Functional, Data Management, Data Science, Distributed Computing, Graph Search, MCP - Microsoft Certified Professional, Machine Learning, Maintain Compliance, Microservices, Performance Tuning/Optimization, Product Engineering, Production Systems, Python Programming/Scripting Language, Redis, Scalable System Development, Systems Reliability, Team Player