Applied AI Engineering Lead

KANINI Software Solutions•Nashville, TN

17h•Hybrid

About The Position

We are looking for an experienced Applied AI Engineering Lead with 6 to 8 years of overall experience to actively engage in the engineering craft, taking a hands-on approach to multiple high-visibility projects. Your expertise will be pivotal in delivering solutions that delight customers and users, while also driving tangible value for business investments. You will leverage your engineering craftsmanship and proficiency across multiple programming languages and modern frameworks, consistently demonstrating a strong track record in delivering high-quality, outcome-focused solutions. The ideal candidate will be a role-model leader and mentor, collaborating with cross-functional teams to design, develop, and deploy advanced software solutions, while actively leveraging AI and agentic tools across the engineering lifecycle to improve speed, quality, and consistency of delivery.

Requirements

Bachelor’s degree in Computer Science, Software Engineering, Data Science, Machine Learning, or a related discipline.
6 to 8 years of experience with most of the following: Angular, React, NodeJS, Python, C#, .NET, Java, SQL/NoSQL, PyTorch, TensorFlow, LangChain, LangGraph, as well as unit testing frameworks.
2+ years of experience building AI/ML applications, with hands-on GenAI experience across LLM integration (OpenAI, Anthropic, or open-source models), RAG pipelines, prompt engineering, and vector databases.
2+ years of experience with cloud-native engineering, using FaaS, PaaS, or micro-services on any of the cloud hyperscalers such as Azure, AWS, or GCP, including their AI/ML services such as Azure OpenAI, AWS Bedrock, or Vertex AI.
Experience establishing or contributing to engineering standards, including mentoring and guiding team members in the adoption and continuous improvement of these standards.
Prior software engineering experience with understanding of Business Context Diagrams (BCD), sequence/activity/state/entity relationship/data flow diagrams, OOP/OOD, data structures, algorithms, code instrumentation, and AI-augmented spec-driven development.
Prior experience using methodologies and tools such as XP, Lean, DevSecOps, SRE, ADO, GitHub, SonarQube, MLflow, and agentic AI frameworks (e.g. LangFuse, LangSmith, or equivalent multi-agent orchestration tools) to deliver high-quality products rapidly.
Ability to work in your local office at a minimum of 3 days per week.
Limited immigration sponsorship may be available.
Ability to travel 0-10%, on average, based on the work you do and the clients and industries/sectors you serve.
Ability to work independently and collaborate as part of a team.
Effective written and verbal communication skills.
Meticulous attention to detail and quality of work product.
Ability to build and sustain professional relationships.
Ability to lead projects or workstreams.
Ability to manage and prioritize multiple tasks in a fast-paced and dynamic environment.
Strong interpersonal skills and professional demeanor.
Ability to meet deadlines.
Ability to mentor and provide clear guidance to others.

Responsibilities

Embrace and drive a culture of accountability for customer and business outcomes, developing engineering solutions that solve complex problems with valuable outcomes, ensuring high-quality, lean designs and implementations.
Serve as a technical advocate for products, ensuring code integrity, feasibility, and alignment with business and customer goals.
Lead requirement analysis, low-level architecture and component design, development, testing, integrations, and support.
Maintain accountability for the integrity of architecture and technical stack to enterprise standards, managing dependencies, code design, implementation, quality, data, and ongoing maintenance and operations.
Stay hands-on, self-driven, and continuously learn new approaches, languages, frameworks, and AI-enabled engineering practices.
Create technical specifications, write high-quality, supportable, scalable code, and review code of other engineers, mentoring them to ensure quality KPIs are met or exceeded.
Develop lean engineering solutions through rapid, inexpensive experimentation, including AI-built prototypes, to solve customer needs.
Engage with customers and product teams before, during, and after delivery to ensure the right solution is delivered at the right time.
Adopt a mindset that favors action and evidence over extensive planning, navigating complexity and uncertainty to deliver lean, supportable, and maintainable solutions.
Work collaboratively with empowered, cross-functional teams including product management, experience, and delivery, integrating diverse perspectives to make well-informed decisions.
Apply AI and Agentic SSDLC practices to support rapid, automated deployments with quality checks embedded across the development lifecycle.
Use AI tools for code generation, review, testing, and documentation to accelerate delivery while maintaining quality standards.
Quickly acquire domain-specific knowledge relevant to the business or product, translating business and user needs into technical specifications and code.
Exhibit strong communication skills, articulating complex technical concepts clearly and influencing teammates and product teams through well-structured, evidence-based arguments.
Engage and collaborate with product engineering teams at all organizational levels, building constructive relationships and fostering a culture of co-creation.