What are the responsibilities and job description for the AI Solutions Engineer position at IMR Soft LLC?
Position: Senior AI Solutions Engineer
Location: New York City, NY (local or nearby candidates only)
Duration: 12-month contract
Role Overview
AI adoption across the firm is accelerating, yet many initiatives stall between prototype and production—or are rebuilt repeatedly in isolation. This role exists to change that trajectory and scale impact.
We are seeking a Senior AI Solutions Engineer with deep full-stack engineering experience who has delivered real-world production systems and now uses AI as a core accelerator—not a novelty. This is not a single-application role. You will design and deliver reusable AI frameworks, patterns, starter kits, and reference architectures that empower teams across the organization to build AI-enabled solutions faster, safer, and at scale.
Operating at the intersection of software engineering, applied AI, and platform architecture, you will focus on:
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Delivering production-grade AI-powered systems
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Converting complex AI implementations into scalable, reusable building blocks
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Eliminating technical, architectural, and process bottlenecks
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Enabling and upskilling engineers to adopt AI responsibly and effectively
Success in this role is measured by:
The number of teams you unblock and enable
The speed at which AI solutions move from idea to production
Adoption and reuse of the platforms, patterns, and frameworks you create
Key Responsibilities
Build and Scale AI Solutions
Design, build, and deploy production-grade AI-enabled applications using modern full-stack and cloud-native architectures
Develop reusable AI frameworks and reference implementations (e.g., RAG, document processing pipelines, agent and workflow patterns)
Integrate AI capabilities into existing enterprise platforms and workflows with strong engineering discipline, including clean code, automation, observability, and reliability
Apply AI with an AI-First Mindset
Leverage AI to accelerate delivery, reduce friction, and maximize engineering productivity
Implement applied GenAI patterns such as Retrieval-Augmented Generation (RAG), prompt and tool orchestration, agentic workflows, and evaluation with guardrails
Design model-agnostic, future-proof solutions resilient to rapid changes in the AI ecosystem
Enable Teams and Drive Engineering Excellence
Translate complex AI implementations into simple, repeatable, and scalable patterns
Mentor engineers and lead architecture and design reviews to improve consistency and quality
Partner with business and technical stakeholders to define requirements and deliver clear, shippable milestones
Own DevOps excellence, including CI/CD pipelines, automated testing, telemetry, monitoring, and continuous improvement
Required Qualifications
AI-first builder mindset with a focus on reusable, scalable solutions and clear technical communication
6 years of experience building, deploying, and operating production-grade full-stack systems at scale
Strong experience with distributed, cloud-native architectures (APIs, data platforms, event-driven systems)
Solid foundation in system design, scalability, reliability, security, and observability
Hands-on, production experience developing AI or GenAI-powered applications (beyond experiments or POCs)
Applied GenAI expertise, including RAG, LLM integration and orchestration, prompt design, and evaluation with guardrails
Proficiency in Java and/or Python using modern frameworks (e.g., Spring Boot, Python-based services)
Experience with CI/CD pipelines, automated testing, and production observability tools
Preferred Qualifications
Public cloud experience, with Azure strongly preferred
Experience building internal platforms, frameworks, or developer tooling
Familiarity with vector databases, embeddings, Kafka, or high-throughput messaging systems
Background in regulated industries or financial services environments
Experience collaborating with globally distributed engineering teams
Salary : $65 - $70