Senior Engineer (Applied AI / ML)
Summary
This role involves combining AI engineering with technical leadership to build production-ready AI solutions, including agentic systems and LLM applications. You will work with clients to identify opportunities, shape requirements, prototype, and deploy secure, scalable AI products, focusing on retrieval, evaluation, and safety.
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.
Accountabilities
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AI discovery and opportunity assessment: Facilitate client workshops, interviews, and collaborative design sessions to identify operational challenges that could benefit from AI solutions.
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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.
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Technical specifications and architecture: Translate business requirements into product requirements documents, technical specifications, system architectures, and measurable evaluation criteria.
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Rapid prototyping: Build functional proof-of-concept applications and interactive demonstrations within short development cycles to validate ideas and gather stakeholder feedback.
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Production engineering: Transform successful prototypes into secure, maintainable, scalable applications by strengthening architecture, testing, observability, evaluation, and operational readiness.
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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.
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Full-stack product delivery: Develop and integrate front-end experiences, backend services, APIs, data infrastructure, and cloud deployments to deliver complete AI-powered products.
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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.
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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.
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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.
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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.
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Cross-functional collaboration: Work closely with client stakeholders, product teams, and technical specialists to refine solutions, demonstrate progress, incorporate feedback, and deliver measurable outcomes.
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Continuous improvement: Identify opportunities to improve AI workflows, development practices, retrieval quality, system performance, and the reliability of deployed applications.
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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.
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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.
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API and system architecture: Experience with REST APIs, WebSockets, streaming APIs, event-driven architectures, and integration protocols such as the Model Context Protocol (MCP).
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Client-facing technical leadership: Demonstrated experience in client-facing engineering, AI solutions architecture, technical product leadership, or similar roles, with the ability to facilitate discovery workshops and present technical solutions to executive audiences.
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Business and product judgment: Ability to translate ambiguous business challenges into actionable technical requirements, challenge assumptions constructively, and recommend simpler or more effective solutions.
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Agentic AI engineering: Hands-on experience building agentic systems, designing orchestration workflows, and understanding when to use established agent frameworks versus custom harnesses and control loops.
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AI-assisted development: Experience with specification-driven AI development tools and agentic coding environments such as Cursor, Codex, or OpenCode to structure requirements, orchestrate code generation, and accelerate end-to-end development.
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Retrieval architecture: Strong expertise in embedding-based search, hybrid retrieval, metadata filtering, reranking, and agentic search patterns, with the ability to measure and optimize relevance, precision, recall, latency, and cost.
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Cloud and DevOps: Experience with at least one major cloud platform, such as AWS, Google Cloud Platform, or Microsoft Azure, as well as containerization technologies such as Docker and Kubernetes and CI/CD automation.
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Production AI systems: Understanding of the reliability, security, governance, evaluation, and integration requirements involved in operating AI/ML applications in production.
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Machine learning foundations: Solid understanding of machine learning concepts and the ability to evaluate pretrained models and determine when fine-tuning, dataset preparation, or custom training pipelines are appropriate.
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Communication and collaboration: Strong interpersonal skills, intellectual curiosity, humility, and a first-principles approach to problem-solving, combined with the ability to build trust with clients and cross-functional teams.
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Experience working in fast-paced consulting delivery teams, client innovation labs, or forward-deployed engineering environments.
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Experience deploying and supporting AI/ML systems within large enterprise environments with complex security, governance, and integration requirements.
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Familiarity with Media & Entertainment, digital media supply chains, enterprise content automation, or related industries.
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Experience applying AI to video, images, audio, and other unstructured or multimodal content for search, classification, transformation, or insight generation.
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Experience rapidly building functional prototypes and user interfaces with AI coding tools such as GitHub Copilot, Claude Code, and OpenCode, while understanding when prototype shortcuts must be replaced with production-grade engineering.
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Willingness to travel to client sites as required by project scope, customer needs, or management direction.
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Healthcare coverage: Medical, dental, and vision insurance.
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Retirement savings: 401(k) plan with employer matching.
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Flexible time off: Flexible vacation policy.
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Remote and hybrid work: Remote or hybrid arrangements may be available depending on project requirements; onsite attendance at client locations may be required.
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Professional development: Sponsored training to strengthen technical skills and support continued career growth.
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Career progression: Emphasis on professional development and individual growth paths.
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Collaborative culture: Opportunity to work with multidisciplinary teams on innovative AI solutions for enterprise clients.
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Compensation: Salary range and additional compensation details were not specified in the provided job description and should be confirmed during the hiring process.
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Travel considerations: Travel to client sites may be required, including locations outside the employee's primary office, state, or region.
Requirements
Preferred qualifications