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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and implementing AI solutions, particularly in Machine Learning and Generative AI, while effectively communicating technical concepts to stakeholders. Proficient in applying MLOps practices and developing intelligent agents within multidisciplinary teams.
Highest-signal resume keywords
Machine Learning Model DevelopmentGenerative AI Solutions DesignLarge Language Models ExpertiseMLOps Practices ImplementationStatistical Methods Proficiency
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine LearningDeep LearningPythonPrompt EngineeringRetrieval-Augmented GenerationStatistical MethodsData ScienceAI DevelopmentModel EvaluationAI Agents Development
Soft Skills
Analytical ThinkingEffective CommunicationCollaborationProblem SolvingStakeholder Engagement
Tools & Technologies
Scikit-learnPyTorchTensorFlowHugging FaceCloud AI PlatformsAI FrameworksAgentic ArchitecturesVersioning ToolsMonitoring ToolsObservability Tools
Industry Keywords
Artificial IntelligenceData ScienceMachine Learning LifecycleGenerative AIAgentic AIMLOpsLLMOpsAgentOpsBusiness Value CreationTechnical Stakeholders
Tech Stack
Tools & technologiesCloudPythonPyTorchScikit-LearnTensorflow
About the role
Key responsibilities & impact- Translate complex business needs into actionable AI and Data Science solutions
- Identify opportunities where Machine Learning, Generative AI, and Agentic AI can create business value
- Explore and evaluate emerging AI methodologies, architectures, and technologies
- Work with clients and stakeholders to define AI use cases and communicate findings and recommendations
- Design and implement Generative AI solutions using LLMs, embedding models, RAG, prompt engineering, and model adaptation
- Design, develop, train, and evaluate predictive and analytical Machine Learning models
- Develop intelligent agents and multi-agent systems with reasoning, planning, tool integration, and human oversight
- Define and implement evaluation methodologies for models, LLM-based systems, and agentic systems
- Lead AI solutions from exploratory analysis and prototyping through deployment and enterprise integration
- Apply MLOps, LLMOps, and AgentOps practices, including versioning, testing, monitoring, observability, and continuous improvement
- Leverage Python, Machine Learning and Deep Learning frameworks, cloud AI platforms, and GenAI and agentic frameworks
- Incorporate security, privacy, explainability, governance, and responsible AI principles
- Collaborate with architects, engineers, domain experts, and business stakeholders
- Contribute to architectural and technical decisions and support junior Data Scientists and AI practitioners
- Participate in technical discussions, workshops, demonstrations, and solution design activities with customers
Requirements
What you’ll need- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Mathematics, Statistics, Engineering, or a related technical field
- Solid professional experience in Data Science, Machine Learning, or AI development
- Proven experience designing, developing, and evaluating Machine Learning models
- Strong understanding of statistical methods, supervised and unsupervised learning, and Deep Learning techniques
- Hands-on experience with Large Language Models, prompt engineering, embeddings, and Retrieval-Augmented Generation (RAG)
- Practical knowledge of AI agents and agentic architectures, including tool integration, orchestration, reasoning workflows, and agentic development frameworks
- Strong proficiency in Python and relevant Data Science and AI libraries, such as Scikit-learn, PyTorch, TensorFlow, or Hugging Face
- Experience across the AI development lifecycle, including data preparation, experimentation, model evaluation, deployment, and monitoring
- Familiarity with MLOps, LLMOps, and emerging AgentOps practices
- Strong analytical thinking and ability to translate business challenges into technical AI solutions
- Ability to work independently and collaboratively within multidisciplinary teams
- Ability to communicate technical concepts effectively and engage with business and technical stakeholders
Benefits
Comp & perks- Flexible, supportive environment
- Well-being support through Be Well programs covering financial, mental, physical, and social health
- Personalized development goals and continuous feedback
- Certifications with Microsoft, Google, and Amazon
- Coaching and hands-on learning experiences
- Cutting-edge learning opportunities
- Career pathing and professional development support
- Employee referral program