Senior AI Systems Analyst
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Job Description
Senior AI Systems & Agent Developer at EVERSANA delivering innovative AI solutions. Leading projects and collaborating with teams to drive AI agent development and integration.
Responsibilities:
- Collaborate with stakeholders to understand business needs and translate them into detailed requirements and agent specifications.
- Analyze and document existing process to identify areas for improvement and automation.
- Create and maintain comprehensive documentation, including requirements, system design, and user manuals.
- Design, develop, and deploy basic autonomous AI agents using generative AI tools and frameworks such as Gemini Enterprise and Copilot.
- Integrate AI agents with existing business software, tools, and APIs.
- Review test plans to ensure the quality and reliability of AI agent outputs against expected business outcomes.
- Coordinate with users to conduct User Acceptance Testing (UAT) and obtain sign-off for deployment.
- Oversee the rollout and deployment of AI agents into production environments.
- Act as the primary point of contact for stakeholders, providing regular updates on project progress.
- Communicate complex concepts, process and solutions to both technical and non-technical audiences.
- Manage stakeholder expectations and ensure that the final solution meets their needs.
- Collaborate with development team to translate business needs to requirements.
- Foster a culture of innovation, collaboration, and continuous learning within the team.
- Stay up-to-date with the latest advancements in generative AI, machine learning, and related technologies.
Requirements:
- 5+ years of experience as a Systems Analyst and a 2+ years focus as an AI Developer.
- Proficiency in AI tools and frameworks such as Gemini Enterprise and Copilot.
- Experience fine-tuning transformer models using frameworks such as PyTorch.
- Familiarity with vector databases (e.g., Pinecone, FAISS, Weaviate, Chroma) and RAG pipelines.
- Strong grasp of prompt engineering, model evaluation, and RLHF or feedback learning methods.
- Understanding of cloud-native ML deployment (Docker, Kubernetes, Vertex AI pipelines).
Benefits:
- Health insurance
- Professional development opportunities
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