Senior 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

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

Qualifications:

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

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

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, sex, gender, gender expression, sexual orientation, age, marital status, veteran status, or disability status. We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform crucial job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.