Salesforce AI / Einstein Developer

OpenKyber LLC
United States
3 months ago

Role details

Contract type
Temporary contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
6 years minimum
Working hours
Regular working hours
Job source

Tech stack

Agile Methodology Artificial Intelligence JIRA Continuous Integration Extract Transform Load (ETL) Software Debugging DevOps Python (Programming Language) Machine Learning Performance Tuning Salesforce.Com Software Engineering
+11 more
Data Streaming Systems Integration Data Ingestion Genesys Git Semi-structured Data Kubernetes Information Technology Teamcity Data Pipelines Jenkins

Requirements

  • 15+ years in software development
  • 3 6 years hands-on AI/ML experience
  • Strong Python experience
  • Hands-on with Hybrid RAG and Natural Language Query (NLQ)
  • Experience delivering production-grade AI solutions
  • Hands-on integrations and data ingestion from Genesys Cloud, Salesforce, and internal/external APIs
  • Skilled in monitoring, debugging, and optimizing data pipelines/data flows
  • Experience with data modeling, performance tuning, and working with structured & semi-structured data (ETL/analysis)
  • Exposure to data science and compute environments like Kubernetes
  • Experience with Agile and DevOps practices
  • Familiar with JIRA
  • Experience with Git and CI tools (Jenkins/TeamCity)
  • Strong communication and problem-solving skills

Desired skills

  • Experience in Python, Hybrid RAG, NLQ Interface
  • Wealth Management and Contact Center experience

Educational Qualification: Minimum BS degree in Computer Science, Engineering, or a related field.

Benefits & conditions

  • Develop and enhance an AI insights platform using Python, Hybrid RAG (semantic + keyword retrieval), and grounded LLM workflows to extract, categorize, and summarize insights from enterprise text and knowledge sources.
  • Apply data science techniques (exploratory analysis, feature engineering, statistical methods, clustering/classification, topic modeling, sentiment analysis, and evaluation metrics) to improve insight quality, relevance, and accuracy; design experiments and track model/LLM performance over time.
  • Build an NLQ (Natural Language Query) interface and robust API integrations to connect the platform with upstream and downstream systems, enabling automated workflows and seamless consumption of AI outputs.
  • Create and maintain a dashboard (React + backend APIs) to visualize trends, classifications, and key metrics; deliver scalable, production-ready services with testing, code reviews, CI/CD, and operational support in an agile, cross-functional environment.

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