Specialist II - Data Science

TekWissen LLC

Los Angeles, CA

JOB DETAILS
SALARY
$57.91–$57.91
SKILLS
Access Control, Amazon Web Services (AWS), Analysis Skills, Apache Spark, Application Programming Interface (API), Artificial Intelligence (AI), Authentication, Best Practices, Caching, Circuit Breakers, Communication Skills, Computer Programming, Computer Science, Continuous Deployment/Delivery, Continuous Improvement, Continuous Integration, Create Graphs, Cross-Functional, Data Management, Data Science, Distributed Computing, Diversity, Docker, Ecosystems, Information Technology & Information Systems, Information/Data Security (InfoSec), Knowledge Engineering, MCP - Microsoft Certified Professional, Maintain Compliance, Performance Tuning/Optimization, Problem Solving Skills, Product Engineering, Production Systems, Python Programming/Scripting Language, Quality Metrics, Redis, Scalable System Development, Systems Reliability, Team Player, Workforce Management
LOCATION
Los Angeles, CA
POSTED
1 day ago
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:
  • 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
  • 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.

About the Company

T

TekWissen LLC

WE THE TEKWISSEN PEOPLE

TekWissen offers you a broader portfolio of services, industry-leading solutions, and the meaningful innovations that give you greater flexibility and speed to respond to market dynamics, reduced costs and risk to improve enterprise performance, and increased productivity to enable growth.

To keep pace with global market demands, TekWissen keeps its finger on the pulse of change. Our organized approach to guiding a project from its inception to closure. Managing projects is becoming more and more important as we enter the digital era. To cope with the pace that this transition demands, a method is required to manage projects so they can yield quality work, while incorporating efficient use of time and resources.

Project involves identifying which quality standards are relevant to the project and determining how to satisfy them.

It is important to perform quality planning during the Planning Process and should be done alongside the other project planning processes because changes in the quality will likely require changes in the other planning processes, or the desired product quality may require a detailed risk analysis of an identified problem. It is important to remember that quality should be planned, designed, then built in, not added on after the fact.

Capabilities and accomplishments in one TekWissen business enhance the opportunity for success in the others. Put simply, TekWissen's unique combination of attributes promotes success.



COMPANY SIZE
100 to 499 employees
INDUSTRY
Computer/IT Services
FOUNDED
2009
WEBSITE
http://www.tekwissen.com/