Description
Title:Senior AI Software Engineer (Agentic AI)
Location: San Francisco, CA
Duration: 3 months
Work Engagement: W2
Work Schedule: Hybrid 3 days in office/2 days remote
Compensation: $110-$128/hr is the pay range that the employer reasonably expects to pay for this position
Benefits on offer for this contract position: Health Insurance, Life insurance, 401K and Voluntary Benefits
Summary:
In this contingent resource assignment, you may: Consult on complex initiatives with broad impact and large-scale planning for Specialty Software Engineering. Review and analyze complex multi-faceted, larger scale, or longer-term Specialty Software Engineering challenges that require in-depth evaluation of multiple factors, including intangibles or unprecedented factors. Contribute to the resolution of complex and multi-faceted situations requiring solid understanding of the function, policies, procedures, and compliance requirements that meet deliverables. Strategically collaborate and consult with client personnel. Required Qualifications: Specialty Software Engineering experience, or equivalent demonstrated through one or a combination of the following: work or consulting experience, training, military experience, education.
Key Responsibilities:
1. Build and Enhance AI Agents
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Develop, deploy, and maintain Agentic AI solutions.
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Create AI agents that automate and orchestrate marketing campaign activities.
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Support the scaling of the platform from the current agents to a larger enterprise ecosystem.
2. Develop Backend Services in Python
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Design and build Python-based backend applications and microservices.
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Write high-quality, production-ready, scalable code.
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Implement asynchronous and distributed processing solutions.
3. Design and Integrate APIs
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Develop REST APIs and service integrations.
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Enable communication between AI agents and enterprise systems.
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Support API performance, reliability, and security.
4. Implement LLM and Generative AI Solutions
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Build applications powered by Large Language Models (LLMs).
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Develop Agentic AI workflows and multi-agent systems.
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Create and optimize prompts, tool-calling, and model orchestration strategies.
5. Develop RAG and Knowledge Retrieval Systems
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Build Retrieval-Augmented Generation (RAG) solutions.
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Implement semantic search using embeddings and vector databases.
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Design hybrid and knowledge graph-enhanced retrieval architectures.
6. Optimize Performance and Scalability
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Improve application performance through profiling and tuning.
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Implement caching and database optimization strategies.
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Design highly available and fault-tolerant systems.
7. Manage Data and Database Solutions
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Work with MongoDB and vector databases.
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Design efficient data retrieval and storage patterns.
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Support embedding pipelines and semantic retrieval capabilities.
8. Apply AI Governance and Responsible AI Practices
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Implement AI guardrails and monitoring solutions.
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Support audit logging, model usage tracking, and compliance requirements.
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Help reduce hallucinations and improve AI response quality.
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Implement controls such as PII redaction and human-in-the-loop validation.
9. Collaborate with Engineering Teams
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Participate in daily standups and technical discussions.
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Work closely with developers and architects on solution design.
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Communicate technical blockers, risks, and dependencies.
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Provide support for production AI systems.
10. Continuously Improve AI Solutions
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Evaluate model performance and accuracy.
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Optimize latency, cost, and user experience.
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Contribute to feedback loops that improve AI agent effectiveness over time.
Key Requirements:
- Applicants must be authorized to work for ANY employer in the U.S. This position is not eligible for visa sponsorship.
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Python: 5+ years
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Microservices: 5+ years
- API Development/Integration: 5+ years
- MongoDB: 5+ years
- Agentic AI: 2+ years preferred