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Senior AI Engineer
Job Location:
Job Location:
Aliso Viejo, CA - USA
Monthly Salary:
Not provided by the employer
Monthly Salary:
Posted:
19 June 2026 (30+ days ago)
Posted:
Application Deadline:
16 September 2026
Application Deadline:
Vacancies:
1 Vacancy
Vacancies:
Job Summary
Overview:
TekWissen is a global workforce management provider headquartered in Ann Arbor Michigan that offers strategic talent solutions to our clients world-wide. Our client provider of digital technology and transformation information technology and services
Position: Senior AI Engineer
Location: Aliso ViejoCA
Duration: 6 Months
Job Type: Temporary Assignment
Work Type: Hybrid
Job Description :
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.
- 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:
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
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
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
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
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
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
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
TekWissen Group is an equal opportunity employer supporting workforce diversity.
