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Ai Implementation Jobs in Alabama (NOW HIRING)

AI Engineer

Birmingham, AL · On-site

$50K - $112K/yr

Your work will involve designing AI systems, data wrangling, and software implementation to enable the AI models to be useful and scalable. As an Associate, you will focus on learning and ...

Forhyre is a leading technology solutions provider that specializes in developing and implementing AI-driven cloud solutions for businesses across various industries. We are committed to delivering ...

Generative AI Strategist

Montevallo, AL

$119K - $154K/yr

Develop comprehensive roadmaps to ensure the effective implementation of generative AI solutions. Business Conceptualization: * Engage in discussions about intricate industry-specific concepts with ...

Evaluate, select, and implement tools (e.g., OpenAI API, Microsoft Copilot, Claude). • Data ... Develop an AI roadmap (scope, prioritization, coordination) with internal stakeholders. • ...

Define and implement protective mechanisms to defend against risks unique to AI. * Secure AI agents and integration points: Provide architecture leadership for the safe design of AI agent frameworks ...

Support RMF activities across all phases, including documentation, control implementation, security assessments, and continuous monitoring. Provide technical guidance on AI model lifecycle management ...

New

... implementation, and measurable business value from AI across the organization. This role will work closely with the AI Steering Committee, executive leaders, technology, legal, IT, security, HR, and ...

Interface with Program Managers and Technical Experts to implement and maintain appropriate AI data segregation across classified programs. * Interface with Information Assurance to ensure security ...

Interface with Program Managers and Technical Experts to implement and maintain appropriate AI data segregation across classified programs. * Interface with Information Assurance to ensure security ...

New

Showing results 21-40

Ai Implementation information

How to get into AI implementation?

To pursue a career in AI implementation, develop strong skills in programming languages such as Python, understand machine learning frameworks like TensorFlow or PyTorch, and gain experience with data analysis and model deployment. Earning relevant certifications or degrees in computer science, data science, or AI can also enhance your qualifications.

What are the key skills and qualifications needed to thrive in the AI implementation position?

To excel in AI Implementation, you need a robust understanding of machine learning concepts, data analysis, and software development, often supported by a degree in computer science or a related field. Familiarity with tools such as Python, TensorFlow, cloud platforms (AWS, Azure), and AI integration frameworks is commonly required, along with relevant certifications. Strong project management, problem-solving abilities, and excellent communication skills are crucial for coordinating with stakeholders and driving adoption. Mastering both technical and interpersonal skills ensures projects are delivered effectively and meet business objectives within diverse organizational settings.

What is an AI implementation?

An AI Implementation job involves deploying artificial intelligence solutions within an organization to improve efficiency, automation, and decision-making. Professionals in this role work closely with data scientists, engineers, and business teams to integrate AI models into existing systems. They manage data pipelines, ensure model performance, and address challenges related to scalability and compliance. Strong technical skills, project management, and an understanding of business processes are essential for success in this role.

What kinds of teams and departments does an AI implementation professional typically collaborate with?

AI Implementation professionals usually work cross-functionally, interacting with data scientists, software engineers, IT departments, and business stakeholders to ensure AI solutions address specific business needs. Regular collaboration with product managers and operations teams helps align technical efforts with strategic objectives and regulatory requirements. You may also work closely with end users to gather feedback, refine implementations, and ensure a smooth adoption process. This collaborative environment not only enhances the quality of AI deployments but also offers valuable exposure to different aspects of the organization, fostering professional growth.

How to become an AI implementation specialist?

To become an AI implementation specialist, individuals typically need a strong background in computer science, data science, or related fields, along with knowledge of AI and machine learning algorithms. Gaining experience with programming languages like Python, familiarity with AI frameworks such as TensorFlow or PyTorch, and understanding of deployment environments are essential. Certifications in AI or cloud platforms can also enhance job prospects in this role.

What are the most commonly searched types of Ai Implementation jobs in Alabama?

The most popular types of Ai Implementation jobs in Alabama are:

What are popular job titles related to Ai Implementation jobs in Alabama?

For Ai Implementation jobs in Alabama, the most frequently searched job titles are:

What cities in Alabama are hiring for Ai Implementation jobs?

Cities in Alabama with the most Ai Implementation job openings:

Infographic showing various Ai Implementation job openings in Alabama as of August 2026, with employment types broken down into 73% Full Time, 23% Part Time, and 4% Contract. Highlights an 65% Physical, 3% Hybrid, and 32% Remote job distribution.

Bioinformatics Research Scientist - AI Reviewer

micro1 AI

Birmingham, AL • Remote

$80 - $110/hr

Part-time

Posted 12 days ago


Job description

Role Title: Computational Biology & Cheminformatics Expert


Role Type: Contractor


Location: Remote


micro1 is engaging Computational Biology & Cheminformatics Experts to contribute their expertise to a customer’s computational drug discovery project. In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters.


Scope of Work

  1. Analyze and interpret small-molecule and drug discovery datasets using advanced computational biology, bioinformatics, and cheminformatics methods.
  2. Curate, annotate, and validate chemical and biological datasets (e.g., ChEMBL, PubChem, DrugBank) to support AI-driven discovery platforms.
  3. Evaluate compound-target interactions, ADMET properties, and lead optimization strategies by integrating chemical, biological, and clinical data sources.
  4. Provide expert insights on structure-activity and structure-property relationships (SAR/SPR), medicinal chemistry approaches, and experimental design considerations.
  5. Build and implement code-based benchmark tasks (e.g., terminal/CLI-based environments) that reflect realistic computational drug discovery scenarios.
  6. Develop reproducible environments (e.g., using Docker) and automated testing pipelines to ensure task correctness and solvability.
  7. Assess and review AI-generated outputs for scientific rigor, accuracy, and practical relevance, delivering detailed written feedback and recommendations.


Preferred Qualifications

  1. Advanced expertise in Computational Biology, Cheminformatics, Medicinal Chemistry, Biochemistry, or related fields; advanced degree (PhD, MSc, PharmD) highly valued but not strictly required.
  2. Strong coding proficiency in Python (beyond analysis scripts), with hands-on experience building tools, pipelines, or testable code; familiarity with Git, GitHub, and Docker.
  3. Extensive experience with cheminformatics toolkits and platforms such as RDKit, KNIME, Schrödinger, OpenEye, or MOE.
  4. Proven track record in small-molecule drug discovery, SAR/QSAR evaluation, ADMET prediction, or virtual screening workflows.
  5. Comfort working with public chemical and bioactivity databases and integrating diverse datasets for scientific analysis.
  6. Demonstrated ability to clearly communicate complex chemical and biological concepts in written feedback and reports.
  7. Experience participating in multidisciplinary and/or remote projects; familiarity with AI-assisted coding tools is a plus.