over 1 year ago
Remote, United StatesStaff+
Responsibilities
- Design and build production AI systems for client environments, including retrieval-augmented generation pipelines, agentic workflows, tool use, and model integrations.
- Deploy AI solutions on AWS, Azure, or GCP and optimize them for scalability, reliability, performance, accuracy, and cost.
- Use Python for data preprocessing, model development, automation, and AI workflow implementation.
- Define and build evaluation frameworks for nondeterministic AI systems and establish measurable quality targets.
- Lead technical discovery, solution design, delivery, and tradeoff discussions with client stakeholders and executives.
- Mentor engineers across geographically distributed teams and help expand the organization’s AI capabilities.
- Research emerging AI technologies and create practical recommendations, reusable accelerators, and internal assets.
- Monitor and improve AI systems after deployment while applying responsible AI, privacy, security, and compliance practices.
- Use AI tools such as Claude through the company’s Anthropic partnership and share effective usage practices with teammates.
Requirements
- 8+ years of experience building and shipping production software or data systems.
- 3+ years of experience delivering AI/ML or generative AI solutions that reached real users.
- Hands-on experience with large language models, retrieval-augmented generation, agentic workflows, tool use, prompt and context engineering, and deciding when fine-tuning is appropriate.
- Strong Python skills and experience with modern AI frameworks, orchestration tooling, vector databases, embedding pipelines, and API design for AI-backed services.
- Production cloud experience with AWS, Azure, or GCP, including AI/ML services, containerization, infrastructure as code, and deployment and monitoring at scale.
- Experience designing evaluation frameworks and defining accuracy, latency, and cost targets for AI systems.
- Working knowledge of data privacy, PII handling, model risk, bias, prompt injection, AI-specific attack surfaces, and client compliance requirements.
- Practical understanding of model selection, inference cost, latency tradeoffs, classical machine learning, and the full model lifecycle.
- Ability to conduct technical discovery, translate business problems into AI solution designs, explain tradeoffs to technical and executive audiences, and support pre-sales conversations.
- Experience mentoring engineers, raising AI capability across a team, and working in a fast-paced, entrepreneurial environment.
- Consulting or client-services experience and experience building reusable accelerators or internal assets are preferred.
Benefits
- Remote or hybrid work options.
- Competitive salary, robust benefits, matching 401(k) plan, and substantial paid time off.
- Training, mentorship, industry certifications, tuition reimbursement, recognition programs, and employee awards.
- Hands-on exposure to emerging technologies across multiple industries and participation in the AI Innovators Program.
Tech Stack
Categories
About Sparq
Sparq is a U.S.-based digital engineering consultancy that designs, builds, and supports AI-enabled software, data, and cloud systems for enterprise clients. It delivers custom applications, ML/AI solutions, and modernization services, validated in its internal The Shop practice and implemented via its Intelligence Studio. Headquartered in Atlanta, it serves Fortune 1000 organizations across logistics, real estate, and financial services, holds Snowflake Elite and AWS Advanced partner status, and is a portfolio company of Harvest Partners.