Overview

Line of Service Advisory

Responsibilities

• Build end-to-end ML/AI pipelines (data → model → deployment → monitoring)
• Develop and deploy ML, Deep Learning, NLP, and GenAI models in production
• Design and implement RAG systems — retrieval, chunking, embeddings, vector search, and prompt engineering
• Build Agentic AI solutions — autonomous agents, multi-agent workflows, tool-calling, planning, and memory
• Build and optimize time series forecasting models (demand forecasting, inventory planning)
• Implement MLOps pipelines — CI/CD, model monitoring, drift detection, governance
• Optimize models for performance, cost, and latency
• Integrate AI systems with enterprise APIs, data platforms, and customer-facing applications
• Design scalable LLM inference architectures for efficient deployment
• Collaborate with data scientists, product managers, engineers, and business stakeholders in Agile teams
• Debug, optimize, and enhance ML models for quality and performance improvements
• Mentor team members and present technical findings to diverse audiences
• Stay current with AI/GenAI trends and evaluate emerging tools and frameworks
• Build end-to-end ML/AI pipelines (data → model → deployment → monitoring)
• Develop and deploy ML, Deep Learning, NLP, and GenAI models in production
• Design and implement RAG systems — retrieval, chunking, embeddings, vector search, and prompt engineering
• Build Agentic AI solutions — autonomous agents, multi-agent workflows, tool-calling, planning, and memory
• Build and optimize time series forecasting models (demand forecasting, inventory planning)
• Implement MLOps pipelines — CI/CD, model monitoring, drift detection, governance
• Optimize models for performance, cost, and latency
• Integrate AI systems with enterprise APIs, data platforms, and customer-facing applications
• Design scalable LLM inference architectures for efficient deployment
• Collaborate with data scientists, product managers, engineers, and business stakeholders in Agile teams
• Debug, optimize, and enhance ML models for quality and performance improvements
• Mentor team members and present technical findings to diverse audiences
• Stay current with AI/GenAI trends and evaluate emerging tools and frameworks
• Build end-to-end ML/AI pipelines (data → model → deployment → monitoring)
• Develop and deploy ML, Deep Learning, NLP, and GenAI models in production
• Design and implement RAG systems — retrieval, chunking, embeddings, vector search, and prompt engineering
• Build Agentic AI solutions — autonomous agents, multi-agent workflows, tool-calling, planning, and memory

Qualifications

• Java Authentication and Authorization Service (JAAS), Java Spring
• Optional Skills
• Accepting Feedback, Accepting Feedback, Active Listening, AI Fluency, AI-Human Collaboration, Algorithm Development, Alteryx (Automation Platform), Analytical Thinking, Analytic Research, Big Data, Business Data Analytics, Communication, Complex Data Analysis, Conducting Research, Creativity, Customer Analysis, Customer Needs Analysis, Dashboard Creation, Data Analysis, Data Analysis Software, Data Collection, Data-Driven Insights, Data Integration, Data Integrity, Data Mining {+ 41 more}
• Desired Languages (If blank, desired languages not specified)
• Travel Requirements
• Not Specified
• Available for Work Visa Sponsorship?
• Government Clearance Required?
• Job Posting End Date
• July 20, 2026

Education

• Bachelor’s or
• Master’s degree in Computer Science, Engineering, or related field (60% above)
• Degrees/Field of Study required: MBA (Master of Business Administration), Bachelor of Engineering
• Degrees/Field of Study preferred:
• Certifications (if blank, certifications not specified)