Role: AI System Analyst (AI Monte Carlo)
Client address: Houston TX (Hybrid)
Contract
Experience Required: 10-12 years
Mandatory skills:
Key Responsibilities:
- Solution Architecture: Design and implement advanced Monte Carlo simulation frameworks to solve complex probabilistic problems (e.g. risk assessment optimization or predictive forecasting).
- Client Engagement: Lead discovery sessions with clients to extract and define technical requirements from high-level business goals.
- Cross-Functional Collaboration: Serve as the primary technical liaison between functional business units and core engineering teams to ensure alignment on deliverables.
- End-to-End Delivery: Own the full lifecycle of AI development-from algorithmic design and data modeling to deployment and performance tuning.
- Mentorship & Leadership: Provide technical guidance to junior/mid-level developers while maintaining the self-sufficiency to handle critical individual contributor tasks in agile environments.
Technical Qualifications:
- Core AI & Math: Expert knowledge of Monte Carlo methods (MCMC Sequential Monte Carlo Quasi-Monte Carlo) and their application in AI/ML environments.
- Programming: Mastery of Python or C (high-performance computing experience is a major plus).
- Infrastructure: Solid understanding of cloud-based AI deployment (AWS Azure or GCP) and containerization (Docker/Kubernetes).
- Strategic Thinking: 10 years of experience navigating the trade
Role: AI System Analyst (AI Monte Carlo) Client address: Houston TX (Hybrid) Contract Experience Required: 10-12 years Mandatory skills: AI Monte Carlo Python Key Responsibilities: Solution Architecture: Design and implement advanced Monte Carlo simulation frameworks to solve complex proba...
Role: AI System Analyst (AI Monte Carlo)
Client address: Houston TX (Hybrid)
Contract
Experience Required: 10-12 years
Mandatory skills:
Key Responsibilities:
- Solution Architecture: Design and implement advanced Monte Carlo simulation frameworks to solve complex probabilistic problems (e.g. risk assessment optimization or predictive forecasting).
- Client Engagement: Lead discovery sessions with clients to extract and define technical requirements from high-level business goals.
- Cross-Functional Collaboration: Serve as the primary technical liaison between functional business units and core engineering teams to ensure alignment on deliverables.
- End-to-End Delivery: Own the full lifecycle of AI development-from algorithmic design and data modeling to deployment and performance tuning.
- Mentorship & Leadership: Provide technical guidance to junior/mid-level developers while maintaining the self-sufficiency to handle critical individual contributor tasks in agile environments.
Technical Qualifications:
- Core AI & Math: Expert knowledge of Monte Carlo methods (MCMC Sequential Monte Carlo Quasi-Monte Carlo) and their application in AI/ML environments.
- Programming: Mastery of Python or C (high-performance computing experience is a major plus).
- Infrastructure: Solid understanding of cloud-based AI deployment (AWS Azure or GCP) and containerization (Docker/Kubernetes).
- Strategic Thinking: 10 years of experience navigating the trade
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