- Company Name
- Advanced Resource Managers
- Job Title
- Agentic AI Solutions Engineer
- Job Description
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**Job Title**
Agentic AI Solutions Engineer
**Role Summary**
A contract specialist who designs, prototypes, deploys, and maintains Agentic AI solutions for enterprise clients. Focuses on large‑language‑model (LLM) implementation, prompt engineering, and seamless integration with existing CRM, ERP, and data platforms to deliver measurable business value.
**Expectations**
- Deliver end‑to‑end AI solutions from concept to production within contract terms.
- Translate business requirements into technical specifications and actionable AI prototypes.
- Ensure secure, compliant, and scalable deployment across cloud environments (Azure, AWS, GCP).
- Maintain rigorous documentation of designs, tests, and operational procedures.
**Key Responsibilities**
- Develop, fine‑tune, and deploy LLMs (e.g., GPT‑4, open‑source equivalents).
- Create and refine prompt engineering strategies to boost model accuracy.
- Rapidly prototype AI applications using Python and relevant ML frameworks.
- Build secure API integrations with enterprise systems (CRM, ERP, HRIS, finance platforms).
- Design and maintain data pipelines, manage structured/unstructured data, and leverage vector databases for semantic search.
- Deploy, scale, and monitor AI solutions in cloud environments, applying MLOps best practices.
- Enforce compliance with GDPR, HIPAA, and other regulatory standards, addressing ethical considerations and bias.
- Document technical designs, project plans, and operational procedures.
- Collaborate with research, product, and business teams to align R&D with commercial needs.
- Stay current with AI and ML advancements; evaluate emerging technologies.
**Required Skills**
- Deep knowledge of AI, NLP, and machine‑learning principles.
- Expertise in selecting, fine‑tuning, and deploying LLMs/SLMs.
- Proven prompt engineering and optimization experience.
- Advanced Python programming (prototyping, integration, framework usage).
- Experience with cloud AI services (Azure AI, AWS SageMaker, GCP AI Platform).
- Knowledge of DevOps/MLOps tools (CI/CD, Docker, Kubernetes, Git, MLflow).
- Exposure to Microsoft Copilot Studio, Azure AI Foundry, Semantic Kernel preferred.
- Understanding of data engineering: pipeline design, vector databases, semantic search.
- Strong communication skills for stakeholder engagement and documentation.
**Required Education & Certifications**
- Bachelor’s or Master’s degree in Computer Science, Data Science, AI, or related field.
- Certifications in cloud AI (Azure AI Engineer Associate / AWS Certified Machine Learning – Specialty / GCP Professional Machine Learning Engineer) are highly desirable.
- Continuous learning mindset to keep up with evolving AI technologies.