AI Engineer (On-Site, IN)

Allied Solutions•Carmel, IN

3d•Onsite

About The Position

The AI Engineer architects, fine-tunes, and deploys AI solutions to streamline operations and uncover insights across diverse industries. The role builds predictive models and implements AI-enabled solutions to improve data utilization, decision-making, and operational excellence at Allied. The engineer configures, connects, and extends enterprise AI platforms, commercial products, automation tools, integrations, and lightweight supporting components. The role also builds secure, reusable connections to approved data, services, and business actions; prototypes and validates solutions; and advances assigned work through testing, production launch, and operational handoff. Working with business stakeholders, the AI Architect, platform owners, and other partners, the engineer discovers workflow needs, translates approved use cases into requirements, evaluates technical capabilities, and provides delivery estimates and recommendations. The role implements responsible, reliable, and sustainable solutions, including appropriate evaluation, monitoring, documentation, user enablement, and continuous improvement.

Requirements

Bachelor’s degree in computer science, Artificial Intelligence, Data Science, or a related field; relevant work experience may be considered as an equivalent.
4+ years of professional experience in AI or related fields.
Experience developing and implementing AI models and systems.
Proficiency in programming languages such as Python, R, or Java.
In-depth knowledge of machine learning frameworks such as TensorFlow and PyTorch, and libraries such as scikit-learn and NLTK.
Ability to work with large data sets and complex algorithms.
Proficiency in data structures, statistical modeling, and computer science fundamentals.
Strong problem-solving skills and ability to think algorithmically.
Strong communication skills, including the ability to explain complex technical concepts to non-technical stakeholders.
Analytical and decision-making skills.
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.
Experience with cloud computing services such as AWS, Azure, or Google Cloud.
Portfolio of projects or contributions to open-source projects demonstrating expertise in AI.

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 standards such as Model Context Protocol (MCP).
Build and validate proofs of concept to assess feasibility, usability, performance, and expected value.
Advance assigned solutions through testing, production readiness, launch, and operational handoff.
Troubleshoot implementation issues and work with platform, architecture, security, data, vendor, and business partners to resolve constraints.
Work with business stakeholders and the AI Architect to understand workflows, users, pain points, desired outcomes, and success measures.
Translate approved use cases into implementable requirements and acceptance criteria, identifying data, access, dependencies, and delivery constraints.
Validate proposed future-state workflows through demonstrations, prototypes, and user feedback, including practical implementation considerations and human decision points.
Provide estimates, technical findings, risks, and implementation options to support delivery decisions.
Maintain awareness of Allied's enterprise platforms, approved technologies, data assets, integration capabilities, and reusable services.
Evaluate candidate capabilities through 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; collaborate with the AI Architect and platform owners to follow enterprise patterns and guardrails.
Develop solution recommendations based on business fit, implementation speed, security, integration, cost, scalability, and support needs.
Implement applicable Responsible AI, privacy, security, legal, accessibility, and data-governance requirements.
Define and execute appropriate evaluations, 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 the Enablement Lead and business teams to provide guidance, demonstrations, training, and other adoption support.
Gather usage information, user feedback, business results, and platform or vendor changes to recommend improvements, simplification, scaling, replacement, or retirement.
Create and share reusable configurations, workflow patterns, implementation documentation, and lessons learned.