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Ai Optimization Jobs in Springfield, MA (NOW HIRING)

NAM Info Inc

Technical Program Manager Gen AI

Hartford, CT · On-site

$130K - $168K/yr

... optimal performance and resource utilization Monitor and forecast AI model capacity needs adjusting as necessary to meet project demands Collaborate with infrastructure and operations teams to ensure ...

Network Designs

AI Integrator #1744026

Chicopee, MA · On-site

... energy optimization, and snowfall recovery. This role requires a rare blend of deep technical ... Specialized AI Focus: At least 5 years of hands-on experience specifically dedicated to building ...

Network Designs Inc.

AI Integrator #1744026

Chicopee, MA · On-site

$167K - $196K/yr

... energy optimization, and snowfall recovery. This role requires a rare blend of deep technical ... Specialized AI Focus: At least 5 years of hands-on experience specifically dedicated to building ...

Infosys

Agentic AI Engineer

Hartford, CT · On-site

... optimizing RAG pipelines with hybrid retrieval, semantic chunking, and vector search. Preferred ... Overview Infosys Topaz is an AI-first suite of services, solutions, and platforms designed to ...

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Infosys

Agentic AI Architect

Hartford, CT · On-site

$63.50 - $83.75/hr

... optimization initiatives in line with business requirements and long-term objectives • Establish ... We do it by enabling the enterprise with an AI-powered core that helps prioritize the execution of ...

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All JobsAi Optimization JobsAi Optimization Jobs in Springfield, MA

Ai Optimization information

See Springfield, MA salary details

$40

$59

$81

How much do ai optimization jobs pay per hour?

As of Sep 5, 2026, the average hourly pay for ai optimization in Springfield, MA is $59.44, according to ZipRecruiter salary data. Most workers in this role earn between $43.12 and $73.32 per hour, depending on experience, location, and employer.

What are common challenges faced by professionals in AI optimization roles, and how can they be overcome?

Professionals in AI Optimization often encounter challenges such as balancing model accuracy with computational efficiency, handling large and complex datasets, and staying updated with rapidly evolving algorithms. To overcome these, it's important to collaborate closely with data engineers and domain experts, utilize scalable computing resources, and continuously invest in learning new optimization techniques. Participating in knowledge-sharing forums and leveraging open-source tools can also help address these challenges effectively.

What are the key skills and qualifications needed to thrive as an AI optimization specialist, and why are they important?

To thrive as an AI Optimization Specialist, you need a strong background in computer science, mathematics, and machine learning, often supported by a degree in a related field. Proficiency with programming languages like Python, optimization frameworks (such as TensorFlow or PyTorch), and knowledge of cloud platforms are typically required, along with relevant certifications. Analytical thinking, problem-solving, and effective communication are essential soft skills for translating complex data into actionable solutions. These skills ensure the development of efficient, scalable AI models that drive business value and innovation.

What is the difference between Ai Optimization vs Data Scientist?

AspectAi OptimizationData Scientist
Required CredentialsDegree in Computer Science, Data Science, or related fields; knowledge of AI/ML frameworksDegree in Statistics, Computer Science, or related fields; proficiency in programming and statistical analysis
Work EnvironmentTech companies, AI-focused teams, R&D departmentsResearch institutions, tech firms, finance, healthcare
Employer & Industry UsagePrimarily in AI development, machine learning optimization projectsData analysis, predictive modeling, data-driven decision making

Ai Optimization specialists focus on enhancing AI models' performance and efficiency, often working on machine learning algorithms and deployment. Data Scientists analyze large datasets to extract insights, build predictive models, and support decision-making. While both roles require strong technical skills and knowledge of data and algorithms, Ai Optimization is more specialized in refining AI systems, whereas Data Scientists have a broader scope in data analysis and interpretation.

What is AI optimization?

AI optimization involves developing and applying algorithms to improve the performance, efficiency, or accuracy of artificial intelligence systems. AI optimization specialists often work with machine learning models, tuning parameters, and using tools like Python or TensorFlow to enhance AI capabilities. Strong analytical skills and knowledge of optimization techniques are essential for this role.

Which AI Optimization job is highly paid?

Senior AI Optimization engineers and machine learning engineers specializing in AI model efficiency and deployment tend to have the highest salaries in the field. These roles often require advanced skills in deep learning, programming, and data analysis, and they typically command higher compensation due to their technical complexity and impact on business performance.

What are popular job titles related to Ai Optimization jobs in Springfield, MA?

For Ai Optimization jobs in Springfield, MA, the most frequently searched job titles are:

What job categories do people searching Ai Optimization jobs in Springfield, MA look for?

The top searched job categories for Ai Optimization jobs in Springfield, MA are:

Ai Optimization jobs near you
Infographic showing various Ai Optimization job openings in Springfield, MA as of August 2026, with employment types broken down into 75% Full Time, 22% Part Time, and 3% Contract. Highlights an 63% Physical, 4% Hybrid, and 33% Remote job distribution, with an average salary of $123,633 per year, or $59.4 per hour.

Senior Consultant - AI Product / Business Analyst - UX Engineering

Long Finch Technologies

Bolton, CT

Full-time

Posted 14 days ago

Key responsibilities

  • Gather business requirements and translate them into detailed Jira user stories, acceptance criteria, workflows, and product features.

  • Design, execute, and monitor AI/LLM test scenarios, validate outputs, evaluate prompt variations, and analyze AI performance metrics to ensure product quality.

  • Collaborate with Engineering, UX, Data Science, and Business teams to prioritize AI capabilities and support the delivery of AI-powered products.

Job description

Job Summary

We are seeking an experienced Senior Consultant – AI Product / Business Analyst  to support the development and validation of AI-powered products and solutions . The ideal candidate will have strong experience working as a Business Analyst, Product Analyst, Product Owner, or similar role , along with hands-on experience in AI/LLM evaluation, prompt engineering, AI testing, and product validation .

The candidate will work closely with Business Stakeholders, Engineering, UX, and Data Science teams  to translate business and AI requirements into actionable product features, user stories, workflows, and acceptance criteria. Experience in the Healthcare domain  is highly preferred, along with hands-on AI experience.

Key Responsibilities

  • Gather and analyze business requirements for AI-powered products and solutions.
  • Translate business requirements into detailed Jira user stories, acceptance criteria, workflows, and product requirements .
  • Manage and prioritize product backlogs in collaboration with Product Owners and stakeholders.
  • Design and execute specialized AI/LLM test cases and evaluation scenarios .
  • Validate LLM-generated outputs for accuracy, quality, completeness, relevance, consistency, and reliability .
  • Evaluate different prompt variations and identify opportunities for prompt optimization.
  • Develop and execute test scenarios for AI agents and agentic workflows .
  • Perform AI model/product validation and analyze AI performance metrics.
  • Identify defects, inconsistencies, and quality gaps in AI-generated content and workflows.
  • Collaborate with Engineering, UX, Data Science, and Business teams to define and prioritize AI capabilities.
  • Support Agile ceremonies including backlog refinement, sprint planning, reviews, and retrospectives.
  • Drive continuous improvement of AI product quality and user experience.
  • Document requirements, test results, evaluation findings, and product recommendations.
  • Partner with stakeholders to ensure AI solutions meet business objectives and user needs.

Required Skills & Qualifications

  • 8–10+ years of experience  as a Business Analyst, Product Analyst, Product Owner, Product Consultant, or equivalent role.
  • Hands-on experience working with AI-powered products, Generative AI, LLMs, or AI/ML solutions .
  • Strong experience creating Jira user stories, acceptance criteria, product requirements, and workflows .
  • Hands-on experience with AI/LLM testing and evaluation .
  • Experience evaluating LLM outputs and comparing prompt variations.
  • Strong understanding of prompt engineering and prompt optimization .
  • Experience validating AI-generated content for accuracy, completeness, consistency, and quality.
  • Experience testing or evaluating AI agents and agentic workflows .
  • Strong Agile/Scrum experience.
  • Excellent communication and stakeholder management skills.
  • Ability to work effectively with Engineering, UX, Data Science, Product, and Business teams .

Preferred / Nice-to-Have Skills

  • Healthcare domain experience is highly preferred.
  • Experience in HR technology, learning technology, content creation platforms, or knowledge management solutions .
  • Experience working with AI-powered enterprise applications.
  • Knowledge of AI evaluation methodologies and performance metrics.
  • Experience with Jira and Agile product development environments.
  • Experience contributing to UX/product discovery and user journey analysis.

Top 3 Responsibilities

  1. Requirements & Product Management:  Gather business requirements, create detailed Jira user stories, define acceptance criteria, and manage product backlogs while translating AI product requirements into actionable features and workflows.
  2. AI/LLM Evaluation & Testing:  Plan, execute, and monitor AI/LLM evaluations by designing test scenarios, validating model outputs, evaluating prompts and agentic workflows, analyzing metrics, and driving continuous product quality improvements.
  3. Cross-Functional Collaboration:  Collaborate with Engineering, UX, Data Science, Product, and Business stakeholders to prioritize AI capabilities and ensure successful delivery of AI-powered products.

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