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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing, building, and operationalizing AI and machine learning systems, with a strong focus on production readiness and measurable business impact. Proficient in Python and SQL, with hands-on experience in deploying AI/ML solutions and collaborating effectively in client-facing environments.
Highest-signal resume keywords
AI/ML EngineeringPython ProgrammingMLOps SolutionsCloud EcosystemsData Pipeline Development
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
AI/ML Systems ProductionizationPredictive MLGenerative AIModel PipelinesAPIs DevelopmentObservabilitySecurity GovernanceCost OptimizationPerformance MonitoringDrift Detection
Soft Skills
Effective CommunicationCollaboration
Tools & Technologies
AWSAzureGoogle CloudSnowflakeDatabricksDbtOpenAIAnthropic
Industry Keywords
Enterprise AI SolutionsIntelligent AutomationCloud-Native AI/ML ServicesAgentic AI SystemsSemantic Retrieval Solutions
Tech Stack
Tools & technologiesAWSAzureCloudPythonSQL
About the role
Key responsibilities & impact- Design, build, and operationalize production-ready AI and machine learning systems delivering measurable business impact for enterprise customers
- Translate architecture designs and business requirements into clean, maintainable, well-tested code
- Own end-to-end implementation of AI applications and workflows across predictive ML, MLOps, generative AI, LLM applications, agentic workflows, and intelligent automation
- Implement robust data and model pipelines, deployment and inference patterns, observability, guardrails, and evaluation suites
- Partner with architects, engineers, business stakeholders, and client representatives to refine designs, clarify requirements, explain technical tradeoffs, and integrate solutions with enterprise data, applications, and workflows
- Troubleshoot complex issues and document systems and decisions
- Create reusable accelerators, reference implementations, and delivery playbooks for the Applied AI practice
- Help mature engineering standards and reusable assets for scalable Applied AI delivery
Requirements
What you’ll need- 5+ years of experience in AI/ML engineering, software engineering, data engineering, or a related technical discipline, including at least 3 years designing, building, deploying, or operating AI and machine learning solutions in production
- Strong programming experience in Python and solid working knowledge of SQL
- Proven experience building and productionizing AI/ML systems, including generative AI, LLM-powered applications, predictive ML, or MLOps solutions
- Hands-on experience developing production software, APIs, services, and data or model pipelines supporting training, inference, evaluation, monitoring, and retraining
- Understanding of production AI/ML concerns including evaluation, observability, security, governance, scalability, performance, cost optimization, and operational support
- Experience with modern cloud, data, and AI ecosystems such as Snowflake, Databricks, AWS, Azure, Google Cloud, dbt, and contemporary AI frameworks and providers such as Anthropic or OpenAI
- Ability to collaborate and communicate effectively with architects, engineers, data scientists, platform teams, business stakeholders, and client representatives in a consulting or client-facing delivery environment
- Bachelor's degree in relevant field, or equivalent practical experience (education listed as desired)
- Experience building agentic AI systems, enterprise RAG systems, semantic retrieval solutions, AI applications, intelligent workflow automation, cloud-native AI/ML services, LLM orchestration frameworks, model registries, feature stores, lineage, monitoring, drift detection, AI evaluations, prompt management, AI gateways, DevOps practices, reusable accelerators, modular architectures, libraries, industry solutions, open-source projects, or technical communities (preferred)
Benefits
Comp & perks- Remote-First Work Environment
- 401k plan with company match
- Dental and Vision insurance
- Home Office Equipment Stipend
- Annual stipend for Learning and Development
- Competitive comp
- Excellent benefits
- 4 weeks PTO plan
- 10 Holidays
- Challenging projects
- Mentorship
- Structured development pathways
- Supportive, high-performing global team
- Transparency, autonomy, and continuous improvement