Lead/Architect GenAI Adoption (freelance, part-time)

N-iX is a global software development service company that helps businesses across the globe create next-generation software products. Founded in 2002, we unite 2,400+ tech-savvy professionals across 40+ countries, working on impactful projects for industry leaders and Fortune 500 companies. Our expertise spans cloud, data, AI/ML, embedded software, IoT, and more, driving digital transformation across finance, manufacturing, telecom, healthcare, and other industries. Join N-iX and become part of a team where your ideas make a real impact.

Our client is a leading global healthcare organization, driving innovation in pharmaceuticals, biotechnology, and patient-centric solutions.

We develop advanced data and analytics solutions that help global organizations turn complex data into intuitive digital tools, enabling teams to make informed decisions in their daily operations and long-term strategy. Our projects span modern operational applications, interactive dashboards, and advanced analytics platforms designed to deliver actionable insights across multiple business domains.

We are looking for GenAI Adoption Lead/ AI Architect to lead the discovery and early implementation stages of an enterprise AI-assisted SDLC transformation. This is a hands-on technical role combining GenAI solution architecture, agentic engineering, AI adoption, and technical discovery.

You will lead the evolution of an existing agentic engineering framework into a scalable, production-ready AI-assisted SDLC model,  shaping the architecture, driving adoption, and turning AI capabilities into measurable engineering outcomes.

Key Responsibilities:

Lead the technical discovery and AI adoption assessment, working with engineering teams and stakeholders to evaluate the current SDLC, AI usage, architecture, tooling, processes, and team readiness

Assess the existing AI/agentic engineering framework, identify technical and adoption gaps, and define the target state

Own the definition of the GenAI solution architecture and implementation approach, including integrations with engineering environments, repositories, Jira/Confluence, CI/CD, and related systems

Translate discovery findings into a prioritized transformation roadmap, including dependencies, risks, architecture decisions, and implementation priorities

Define measurable success criteria, select an anchor project and pilot workflows, and establish the baseline against which AI impact will be measured

Lead the design and implementation of agentic workflows, including agents, prompts/skills, context and tool access, guardrails, human-in-the-loop controls, quality gates, and model routing

Define the approach to AI governance, evaluation, observability, telemetry, and auditability in collaboration with security, architecture, and compliance stakeholders

Lead pilot execution end-to-end, validate results against agreed metrics, and turn learnings into reusable AI engineering practices

Required Skills & Experience:

8+ years of experience in software engineering, solution architecture, engineering leadership, or technical consulting, with strong recent focus on Generative AI

Proven experience leading GenAI adoption, AI-assisted SDLC transformation, or enterprise AI engineering initiatives

Strong hands-on knowledge of LLMs, RAG, AI agents, agentic workflows, prompt/context engineering, tool calling, and multi-agent systems

Experience designing enterprise GenAI architectures, including agent orchestration, knowledge/context integration, guardrails, evaluation, observability, and human-in-the-loop patterns

Hands-on experience with AI engineering tools such as Claude Code, GitHub Copilot, Cursor, OpenAI Codex, or similar

Strong understanding of software architecture, SDLC, CI/CD, APIs, cloud environments, and enterprise integrations

Experience leading technical discovery, architecture assessments, gap analysis, and roadmap definition

Experience designing and integrating AI agents into real engineering workflows

Understanding of AI governance, security, telemetry, auditability, and responsible AI practices

Ability to define measurable engineering and AI adoption metrics and evaluate outcomes

Strong leadership, workshop facilitation, stakeholder management, and presentation skills

Ability to independently structure ambiguous initiatives and turn high-level transformation goals into actionable technical solutions

Nice to have:

Experience implementing AI governance in regulated enterprise environments

Experience in pharma, healthcare, or other highly regulated industries