Senior Engineer; Applied AI​/ML

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Engineer (Applied AI / ML) based in the United States.

The Senior Engineer (Applied AI / ML) will combine hands-on AI engineering with technical leadership to transform complex business challenges into practical, production-ready AI solutions. Working directly with client stakeholders, you will identify high-value opportunities, shape technical requirements, and guide projects from early discovery through deployment. You will design and build agentic systems, enterprise AI workflows, retrieval solutions, and applications powered by large language models.

The role emphasizes rapid prototyping while maintaining the engineering rigor required for secure, scalable, and reliable production systems. You will also develop evaluation frameworks, improve AI observability, and implement safeguards that support dependable enterprise adoption. This remote opportunity is ideal for an experienced engineer who thrives in collaborative consulting environments, enjoys solving ambiguous problems, and can connect business priorities with innovative technical solutions.

• AI discovery and opportunity assessment:
  Facilitate client workshops, interviews, and collaborative design sessions to identify operational challenges that could benefit from AI solutions.

• Use case prioritization:
  Evaluate potential AI initiatives based on business impact, technical feasibility, data readiness, and return on investment, recommending practical approaches and prioritized roadmaps.

• Technical specifications and architecture:
  Translate business requirements into product requirements documents, technical specifications, system architectures, and measurable evaluation criteria.

• Rapid prototyping:
  Build functional proof-of-concept applications and interactive demonstrations within short development cycles to validate ideas and gather stakeholder feedback.

• Production engineering:
  Transform successful prototypes into secure, maintainable, scalable applications by strengthening architecture, testing, observability, evaluation, and operational readiness.

• Agentic AI development:
  Design and implement single-agent and multi-agent systems with tool calling, planning, context and state management, delegation, structured outputs, control loops, and failure recovery.

• Full-stack product delivery:
  Develop and integrate front-end experiences, backend services, APIs, data infrastructure, and cloud deployments to deliver complete AI-powered products.

• Retrieval and enterprise integration:
  Build retrieval-augmented generation (RAG) systems and related capabilities, including semantic and hybrid search, embeddings, metadata filtering, reranking, and enterprise data integrations.

• Machine learning and model adaptation:
  Apply machine learning fundamentals to model selection, evaluation, and adaptation, including fine-tuning approaches such as LoRA and parameter-efficient fine-tuning (PEFT) when appropriate.

• AI evaluation and observability:
  Establish evaluation frameworks using offline benchmarks, task-specific metrics, LLM-as-judge techniques, human review, regression testing, and production telemetry to measure quality, latency, cost, and failure patterns.

• AI safety and reliability:
  Implement safeguards for privacy, personally identifiable information (PII), prompt injection, permission-aware tool access, moderation, fallback behavior, and graceful error handling.

• Cross-functional collaboration:
  
  Work closely with client stakeholders, product teams, and technical specialists to refine solutions, demonstrate progress, incorporate feedback, and deliver measurable outcomes.

• Continuous improvement:
  Identify opportunities to improve AI workflows, development practices, retrieval quality, system performance, and the reliability of deployed applications.

• Professional 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.

• Programming expertise:
  Advanced proficiency in Python and strong software engineering fundamentals, including the design of stateless and stateful services for real-time, interactive, and batch workloads.

• API and system architecture:
  Experience with REST APIs, Web Sockets, streaming APIs, event-driven architectures, and integration protocols such as the Model…