Artificial Intelligence Engineer (On-Site, IN)

Allied Solutions•Carmel, IN

20h•Onsite

About The Position

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.

Requirements

Bachelor’s degree in computer science, Artificial Intelligence, Data Science, or a related field is required.
4+ years of professional experience in AI or related fields required.
Experience in developing and implementing AI models and systems.
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.

Nice To Haves

Master's degree preferred.
Relevant work experience may be considered as an equivalent for education requirements.
Experience with cloud computing services (AWS, Azure, Google Cloud) is a plus.

Responsibilities

Configure, connect, and extend AI-enabled solutions using enterprise AI platforms, commercial products, automation tools, integrations, and lightweight supporting components.
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).
Build and validate proofs of concept to confirm feasibility, usability, performance, and expected value before broader implementation.
Advance assigned solutions through testing, production readiness, launch, and operational handoff within the team's delivery priorities and practices.
Troubleshoot implementation issues and collaborate with platform, architecture, security, data, vendor, and business partners to resolve constraints.
Work with business stakeholders and the AI Architect to understand the assigned workflow, users, pain points, desired outcomes, and measures of success.
Translate an approved use case into implementable requirements and acceptance criteria; identify required data, access, dependencies, and delivery constraints.
Validate the proposed future-state workflow through demonstrations, prototypes, and user feedback, surfacing practical implementation considerations and appropriate human decision points.
Provide estimates, technical findings, risks, and implementation options to support solution and delivery decisions.
Maintain a strong understanding of Allied's enterprise platforms, approved technologies, data assets, integration capabilities, and reusable services.
Evaluate candidate capabilities through hands-on research and experimentation, including generative AI, predictive machine learning, rules-based automation, analytics, and existing platforms.
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.
Develop clear solution recommendations based on business fit, implementation speed, security, integration, cost, scalability, and ongoing support needs.
Implement applicable Responsible AI, privacy, security, legal, accessibility, and data-governance requirements.
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.
Implement appropriate monitoring, feedback mechanisms, documentation, and support procedures.
Consider maintainability, platform alignment, vendor dependencies, technical debt, and operational overhead in implementation decisions.
Partner with Enablement Lead and business teams to provide guidance, demonstrations, training, and other support needed for effective adoption.
Gather usage information, user feedback, business results, and relevant platform or vendor changes to recommend improvements, simplification, scaling, replacement, or retirement.
Create and share reusable configurations, workflow patterns, implementation documentation, and lessons learned.