Senior Engineer (Applied AI / ML)
New
J
JobgetherApplied AI/ML
Based in the United StatesFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- At least 5 years of experience in software engineering or technical product engineering, including at least 1 year building and deploying generative AI or large language model (LLM) applications in production.
- Required Skills
- AWSPythonGCPMicrosoft AzureRESTful APIsLLMGenerative AI
Requirements
- At least 5 years of experience in software engineering or technical product engineering.
- At least 1 year building and deploying generative AI or LLM applications in production.
- Advanced proficiency in Python and software engineering fundamentals for stateless and stateful services.
- Experience with REST APIs, WebSockets, streaming APIs, event-driven architectures, and integration protocols such as MCP.
- Client-facing engineering, AI solutions architecture, or technical product leadership experience, including facilitating discovery workshops and presenting to executive audiences.
- Hands-on experience building agentic systems and orchestration workflows, and choosing between established frameworks and custom harnesses or control loops.
- Experience with specification-driven AI development tools and agentic coding environments such as Cursor, Codex, or OpenCode.
- Expertise in embedding-based search, hybrid retrieval, metadata filtering, reranking, and agentic search patterns.
- Experience measuring and optimizing retrieval relevance, precision, recall, latency, and cost.
- Experience with at least one major cloud platform, Docker, Kubernetes, and CI/CD automation.
- Understanding of production AI/ML reliability, security, governance, evaluation, and integration requirements.
- Solid machine learning foundations, including evaluating pretrained models and assessing fine-tuning, dataset preparation, or custom training needs.
- Willingness to travel to client sites as required.
Responsibilities
- Facilitate client workshops and interviews to identify and assess AI opportunities.
- Prioritize AI use cases based on business impact, technical feasibility, data readiness, and return on investment.
- Translate business requirements into product requirements, technical specifications, system architectures, and evaluation criteria.
- Build functional proof-of-concept applications and interactive demonstrations.
- Turn successful prototypes into secure, maintainable, scalable production applications.
- Design and implement single-agent and multi-agent systems with tool calling, planning, state management, and failure recovery.
- Develop and integrate front-end experiences, backend services, APIs, data infrastructure, and cloud deployments.
- Build RAG systems with semantic and hybrid search, embeddings, filtering, reranking, and enterprise data integrations.
- Establish AI evaluation frameworks and production telemetry for quality, latency, cost, and failure patterns.
- Implement safeguards for privacy, PII, prompt injection, tool access, moderation, and error handling.
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