AI Software Engineer

Ness Digital Engineering

About The Position

As an AI Software Engineer, you will be part of a team building AI-powered operational platforms that integrate across monitoring systems, CI/CD pipelines, ticketing tools, and cloud infrastructure. You will work on designing and implementing intelligent workflows, integrating AI models, and building scalable systems that automate complex operational tasks. This is a highly hands-on role focused on building, integrating, and scaling AI-driven solutions in production environments.

Requirements

5+ years of software engineering experience, strong focus on full-stack web development
Expert in TypeScript and React — performance optimization, modern patterns (hooks, context, suspense), component architecture
Production experience with Next.js — App Router, Server Components, API routes, SSR/SSG, edge deployment
Hands-on experience with LLMs — prompt engineering, streaming APIs, function calling, tool-use, chaining and orchestration patterns
Experience with Vercel AI SDK — unified LLM provider interface, streaming, structured output, tool calling across OpenAI/Anthropic/Google/xAI
Model Context Protocol (MCP) — building or consuming MCP servers for extensible AI tool use
Strong backend fundamentals — Node.js or Python, REST/GraphQL APIs, relational databases, Redis, auth
Solid database design — PostgreSQL, Drizzle ORM, schema modeling for complex domains, query optimization, migrations
Experience building scalable, distributed systems in cloud environments (AWS / Azure)
Working knowledge of CI/CD, Docker, Kubernetes
Familiarity with Tailwind CSS, Radix UI and modern component-driven UI development
High agency — you operate independently in ambiguous environments, take ownership of problems, and drive them to completion
Strong problem-solving and analytical skills
Ability to work in a fast-paced, evolving environment
Communicate effectively with both technical and non-technical stakeholders

Nice To Haves

Experience building agentic coding tools, AI agent frameworks, or developer-facing SDKs/APIs (Claude Agent SDK, OpenAI Agents SDK)
Experience with Vercel ecosystem — Next.js, AI SDK providers, Turbopack
Background in evaluation frameworks — measuring model capabilities, collecting human feedback at scale, A/B testing outputs
Experience with sandboxed execution environments for safely running AI-generated code
Built research tools, experimentation platforms, or scientific software
Proficiency with Python — FastAPI/Django, data pipelines, ML tooling
Knowledge of observability tools (Grafana, Prometheus, Sentry, etc.)
Experience building automation or internal platforms
Familiarity with real-time features — WebSockets, streaming UX, collaborative interfaces
Knowledge of advanced web technologies — WebGL, WebAssembly, web workers, PWAs
Experience with alternate JS runtimes — Bun, Deno
Built open-source tools or platforms with active user communities
Strong quantitative foundation (math, physics, or related fields)

Responsibilities

Build and scale AI-driven workflows and automation systems
Develop integrations with systems like monitoring platforms, ticketing tools (ServiceNow, Jira, OpsGenie), CI/CD pipelines, and cloud services
Design and implement APIs, tools, and data pipelines that power AI-driven decision-making
Work on LLM integrations, prompt engineering, and orchestration layers — streaming responses, function calling, tool use, RAG pipelines, agentic orchestration
Build and maintain full-stack AI applications using TypeScript, React, and Next.js — from user dashboards and personalized experiences to real-time analytics and interactive tools
Translate real-world operational problems into automated, intelligent solutions
Collaborate with Product, SRE, and Infrastructure teams to deliver end-to-end capabilities
Improve system performance, reliability, and observability
Build evaluation and observability systems — measure model capabilities, monitor output quality, and create dashboards that keep the product improvable
Create reusable platforms and tools that accelerate development — component libraries, shared abstractions, internal tooling that multiplies team productivity
Design and develop scalable backend systems for AI-powered platforms
Build and maintain AI integrations, workflows, and automation pipelines
Implement REST APIs, microservices, and event-driven architectures
Design and implement database schemas and queries for complex domains — tracking, engagement, reporting
Work with both structured and unstructured data for AI use cases
Contribute to CI/CD pipelines, testing, and deployment automation
Troubleshoot and optimize production systems
Collaborate with cross-functional teams to deliver high-quality solutions
Contribute to reusable frameworks and engineering best practices
Prototype fast — move from concept to working demo in days, ship incrementally