Note: The job is a remote job and is open to candidates in USA. Infinite Computer Solutions is a global digital engineering and IT services company that delivers technology solutions across telecommunications, healthcare, banking, and finance. The Senior AI Productivity Engineer will lead the adoption of AI across the software delivery lifecycle and product operations for Thoriva Loan Platforms, including identifying opportunities, implementing secure AI workflows and agents, enabling teams, and measuring outcomes. The role supports legacy and modern application environments while partnering with security, compliance, and technology leaders to promote responsible AI adoption.
Responsibilities
- Partner with product and technical teams to understand daily workflows, recurring friction, manual effort, quality concerns, and knowledge gaps that may be improved through AI-assisted tools or automation
- Create and maintain an AI productivity roadmap that prioritizes opportunities based on business value, user impact, implementation effort, technical feasibility, security, compliance, and measurable outcomes
- Evaluate new AI productivity products and identify useful AI capabilities within existing enterprise tools; lead structured pilots, document findings, and recommend adoption, refinement, or retirement decisions
- Design, prototype, and implement AI-assisted workflows, agents, integrations, prompts, scripts, and reusable components that support technical and product delivery
- Enable productivity use cases such as code generation and completion, code explanation, refactoring, legacy-code understanding and documentation generation, unit-test generation, code review assistance, defect analysis, debugging support, and secure dependency or vulnerability remediation
- Develop solutions for document ingestion, classification, summarization, comparison, traceability, and knowledge retrieval, including extraction of requirements, business rules, acceptance criteria, dependencies, risks, and open questions from complex source materials
- Apply AI to accelerate creation and maintenance of functional requirements, technical designs, architecture documentation, data mappings, test plans, test cases, test data strategies, release notes, runbooks, knowledge articles, and other software lifecycle artifacts
- Define agent operating boundaries, permissions, data access, escalation paths, approval points, exception handling, observability, and audit requirements so automated actions remain secure, explainable, reversible where appropriate, and accountable to a human owner
- Explore AI-assisted test automation, including generation and maintenance of test scenarios and scripts, regression-impact analysis, synthetic test data support, defect clustering, and analysis of test results across mainframe and distributed applications
- Support modernization and knowledge-transfer activities by helping teams analyze COBOL, JCL, CICS, DB2, batch processing, interfaces, .NET, Java, APIs, databases, and scripts without losing critical business context or established controls
- Define reusable prompting, context-management, retrieval, evaluation, and human-review practices appropriate for regulated, business-critical software environments
- Establish training and enablement plans that include role-based learning, demonstrations, hands-on working sessions, office hours, job aids, champions, and follow-up coaching tailored to different experience and confidence levels
- Work directly with individuals and teams during adoption, listen to concerns, respond constructively to feedback, and adjust tools and methods so improvements are understandable, practical, and sustainable
- Define adoption and outcome measures before implementation; report usage, time saved, cycle-time changes, quality indicators, rework, user feedback, and other relevant results without relying on activity volume alone
- Establish feedback loops and continuously improve solutions based on actual team usage, changing priorities, support needs, model behavior, and lessons learned from production use
- Partner with security, privacy, legal, architecture, compliance, and technology leaders to ensure approved data handling, access controls, intellectual-property protections, auditability, model governance, and responsible AI practices
- Stay current on changes in AI-assisted software engineering, agentic automation, developer tooling, model capabilities, and industry practices; translate relevant developments into practical recommendations for Thoriva teams
Skills
- 7+ years of progressively responsible experience in AI adoption, software engineering, developer productivity, DevOps, test automation, technical enablement, applied AI, or a related technology role, including hands-on delivery of solutions used by technical teams
- Demonstrated ability to help legacy software development teams adopt AI-first practices
- 5C public trust security clearance (or the ability to be granted applicable clearance)
- Demonstrated experience identifying workflow problems, prototyping technical solutions, leading pilots, supporting production adoption, and measuring results
- Hands-on experience with generative AI, agentic AI, and large language model capabilities, including prompt design, structured outputs, RAG (retrieval-augmented generation) implementations, tool use, agent design, multi-step orchestration, MCP (model context protocol) implementations, and human-in-the-loop controls
- Strong software engineering foundation and proficiency with one or more general-purpose or scripting languages such as Python, Java, C#, JavaScript/TypeScript, or comparable technologies
- Experience integrating AI capabilities with repositories, document stores, development environments, issue-tracking systems, CI/CD pipelines, APIs, databases, monitoring platforms, or enterprise collaboration tools
- Working knowledge of the software development lifecycle, requirements analysis, source control, code review, testing, release management, production support, incident management, and technical documentation
- Ability to work across legacy and modern technology environments and to learn unfamiliar platforms, languages, business rules, and operational processes quickly
- Demonstrated ability to develop training materials, facilitate hands-on learning, coach users with varied technical backgrounds, and sustain adoption after initial rollout
- Strong communication, facilitation, listening, and relationship-building skills, with the patience and judgment to help teams learn, challenge ineffective practices constructively, and build trust during change
- Experience defining meaningful productivity, adoption, quality, or engineering-effectiveness measures and communicating results to technical teams and leadership
- Strong understanding of responsible AI, data privacy, secure development, access control, intellectual-property risk, model limitations, and the need for accountable human validation of AI-generated outputs
- Ability to manage multiple initiatives, make practical tradeoffs, document decisions, and operate effectively in a fast-paced, highly regulated, and collaborative environment
- Bachelor's degree in computer science, software engineering, information systems, data science, or related field, or equivalent professional experience
- Experience supporting student loan servicing, financial services, government contracting, public-sector technology, or another highly regulated environment
- Experience evaluating AI usage and token consumption to recommend the most cost-effective tool for each use case
- Advanced degree in AI (i.e., Master of Science in AI)
- Familiarity with mainframe application development and operations, including COBOL, JCL, CICS, DB2, VSAM, batch processing, and integration with distributed systems
- Experience with .NET, Java, APIs, SQL, automated testing, CI/CD, observability, cloud services, and modern application development practices
- Experience with AI coding assistants, enterprise copilots, model APIs, orchestration frameworks, vector databases, semantic search, document intelligence, or agent-development platforms
- Experience designing evaluations for generative AI solutions, including accuracy, safety, security, reliability, latency, cost, and user acceptance
- Experience applying organizational change management, learning, developer experience, product management, or continuous improvement practices to technology adoption
- Experience building secure proof-of-concept solutions and transitioning successful prototypes into governed, supportable enterprise capabilities
- Experience with agent-development or orchestration platforms, model tool-calling, workflow state management, identity and authorization for agents, agent monitoring, and controlled integration with enterprise applications and APIs
- Relevant certifications or formal training in artificial intelligence, cloud platforms, software engineering, DevOps, security, agile delivery, change management, or instructional design
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