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Artificial Intelligence Engineer (On-Site, IN)

Alliedsolutions

Carmel, IN, USonsite

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About this role

An Artificial Intelligence Engineer architects, fine-tunes, and deploys AI solutions to streamline operations and unearth insights, driving innovation across diverse industries. With expertise in machine learning and cognitive technologies, they build predictive models that not only solve current challenges but also anticipate future trends. Their work revolutionizes data utilization, enhancing decision-making and setting new standards for operational excellence.

*Job Duties and Responsibilities:

Hands-On Solution Implementation (50%): • Implementation: Configure, connect, and extend AI-enabled solutions using enterprise AI platforms, commercial products, automation tools, integrations, and lightweight supporting components. • AI Platform Integration: Design and build secure, reusable connections that enable enterprise AI platforms to access approved data, services, and business actions using APIs, connectors, orchestration tools, and emerging standards such as Model Context Protocol (MCP) • Prototyping: Build and validate proofs of concept to confirm feasibility, usability, performance, and expected value before broader implementation. • Production Delivery: Advance assigned solutions through testing, production readiness, launch, and operational handoff within the team's delivery priorities and practices. • Problem Resolution: Troubleshoot implementation issues and collaborate with platform, architecture, security, data, vendor, and business partners to resolve constraints.

Solution Discovery and Delivery Planning (15%): • Implementation Discovery: Work with business stakeholders and the AI Architect to understand the assigned workflow, users, pain points, desired outcomes, and measures of success. • Requirements and Data Readiness: Translate an approved use case into implementable requirements and acceptance criteria; identify required data, access, dependencies, and delivery constraints. • Workflow Validation: Validate the proposed future-state workflow through demonstrations, prototypes, and user feedback, surfacing practical implementation considerations and appropriate human decision points. • Delivery Planning: Provide estimates, technical findings, risks, and implementation options to support solution and delivery decisions.

Platform and Capability Solutioning (15%): • Capability Awareness: Maintain a strong understanding of Allied's enterprise platforms, approved technologies, data assets, integration capabilities, and reusable services. • Capability Fit: Evaluate candidate capabilities through hands-on research and experimentation, including generative AI, predictive machine learning, rules-based automation, analytics, and existing platforms. • Technical Findings: Document feasibility, performance, integration needs, implementation effort, limitations, and support considerations to inform solution decisions; work with the AI Architect and platform owners to ensure the selected approach follows enterprise patterns and guardrails. • Recommendations: Develop clear solution recommendations based on business fit, implementation speed, security, integration, cost, scalability, and ongoing support needs.

Responsible, Reliable, and Sustainable Implementation (10%): • Responsible Delivery: Implement applicable Responsible AI, privacy, security, legal, accessibility, and data-governance requirements. • Testing and Evaluation: Define and execute evaluations appropriate to the solution type, including business effectiveness, usability, accuracy, precision and recall where applicable, groundedness, bias, drift, failure handling, human oversight, and escalation. • Operational Readiness: Implement appropriate monitoring, feedback mechanisms, documentation, and support procedures. • . Sustainable Design: Consider maintainability, platform alignment, vendor dependencies, technical debt, and operational overhead in implementation decisions.

Adoption, Measurement, and Reuse (10%): • User Enablement: Partner with Enablement Lead and business teams to provide guidance, demonstrations, training, and other support needed for effective adoption. • Continuous Improvement: Gather usage information, user feedback, business results, and relevant platform or vendor changes to recommend improvements, simplification, scaling, replacement, or retirement. • Knowledge Reuse: Create and share reusable configurations, workflow patterns, implementation documentation, and lessons learned.

*Qualifications (Education, Experience, Certifications & KSA): • Bachelor’s degree in computer science, Artificial Intelligence, Data Science, or a related field is required. • Master's degree preferred. • Relevant work experience may be considered as an equivalent for education requirements. • 4+ years of professional experience in AI or related fields required. • Experience in developing and implementing AI models and systems. • Experience with cloud computing services (AWS, Azure, Google Cloud) is a plus. • Portfolio of projects or contributions to open-source projects demonstrating expertise in AI. • Proficient in programming languages such as Python, R, or Java. In-depth knowledge of machine learning frameworks (e.g., TensorFlow, PyTorch) and libraries (e.g., scikit-learn, NLTK). • Strong ability to work with large data sets and complex algorithms. Proficient in data structures, statistical modeling, and computer science fundamentals. • Excellent problem-solving skills and the ability to think algorithmically. • Strong communication skills, with the ability to explain complex technical concepts to non-technical stakeholders. • Analytical and decision-making. • Ability to work independently and as part of a team. • Ability to meet deadlines and work under pressure. • Ability to think strategically and tactically.

#LI-ID1   The above statements are intended to describe the general nature and level of work being performed by people assigned to this job. They are not intended to be an exhaustive list of all responsibilit

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