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Specialist II Data Science
Job Location:
Job Location:
Los Angeles, 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: Specialist II - Data Science
Location:Los Angeles CA 90013
Duration: 6 Months
Job Type: Temporary Assignment
Work Type: Onsite
Job Description :
- 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:
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:
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
- 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
TekWissen Group is an equal opportunity employer supporting workforce diversity.
