Senior Applied AI Engineer
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Key skills for this role
About the Role
We are hiring a Senior Applied AI Engineer to design and deploy production-grade AI systems integrated with enterprise platforms such as Oracle, Salesforce, and other business ecosystems.
Key Skills for This Role
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Role Overview
We are hiring a Senior Applied AI Engineer to design and deploy production-grade AI systems integrated with enterprise platforms such as Oracle, Salesforce, and other business ecosystems.
This role requires deep understanding of modern LLM behavior, RAG architectures, system design, and backend engineering — along with the ability to integrate AI into structured enterprise data environments.
This is a 6-Month Contract/Extendable.
1. Applied LLM Engineering
- Design and implement RAG pipelines (chunking, embeddings, retrieval tuning)
- Optimize prompt structures for reliability and consistency
- Implement hallucination mitigation and output validation
- Design guardrails against prompt injection and misuse
- Manage context windows and token optimization strategies
- Build agent-based workflows when required
2. Enterprise Integration
- Integrate AI solutions with enterprise systems, including:
- Oracle
- Salesforce
- CRM / ERP platforms
- Work with structured enterprise datasets (leads, transactions, workflows)
- Design APIs and connectors to fetch and enrich enterprise data
- Handle authentication and secure integrations
- Ensure data governance and compliance standards
- *Experience with Oracle Cloud Infrastructure, Salesforce APIs, or similar enterprise platforms is a strong advantage.*
3. Backend & Architecture
- Develop scalable APIs using FastAPI or similar frameworks
- Integrate vector databases (FAISS, Qdrant, Milvus)
- Work with PostgreSQL, Redis, and other storage systems
- Design async workflows and background processing systems
- Ensure clean, modular, maintainable codebases
4. Production & Deployment
- Deploy AI services on AWS / Azure / OCI
- Containerization using Docker
- Monitor latency, token usage, and cost
- Implement logging and observability for AI outputs
- Optimize inference-heavy workloads
Required Skills
- 5+ years in software engineering or AI engineering
- Strong Python expertise
- Proven experience building LLM-based systems in production
- Strong understanding of RAG architectures, embeddings, and retrieval strategies
- Familiarity with token economics and latency trade-offs
- Knowledge of prompt injection risks and hallucination behavior
- Experience integrating APIs with enterprise systems
- Strong system design knowledge
- Hands-on experience with video generation models (like Veo, Seedance) and with running/integrating local LLMs and local LLM frameworks
Strong Advantage
- Experience working with Oracle ecosystem
- Experience working with Salesforce ecosystem
- Experience building AI-driven CRM enrichment pipelines
- Experience handling large-scale enterprise data flows
- Prior experience in high-ownership AI roles
Ideal Candidate Profile
- We are looking for someone who:
- Is among the top tier in applied AI system knowledge
- Understands real-world LLM nuances beyond surface-level prompting
- Has shipped AI systems into enterprise environments
- Can independently own architecture decisions
- Thinks in terms of reliability, scale, and governance
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