Apexon - AI Implementation Architect

• Apexon - AI Implementation Architect
• Company: Apexon
• Job Location: Maharashtra, India
• Employment Type: Full-time
• Experience Required: 10+ years
• About Apexon :

Apexon is a digital-first technology services firm specializing in accelerating business transformation and delivering human-centric digital experiences. We have been meeting customers wherever they are in the digital lifecycle and helping them outperform their competition through speed and innovation.

Apexon brings together distinct core competencies in AI, analytics, app development, cloud, commerce, CX, data, DevOps, IoT, mobile, quality engineering and UX, and our deep expertise in BFSI, healthcare, and life sciences to help businesses capitalize on the unlimited opportunities digital offers.

About Role :

AI Implementation Architect

This role is a hands-on, embedded individual contributor that acts as a guide and coach to product and engineering teams as they adopt AI across the Product Development Lifecycle (PDLC). Rather than managing teams or owning delivery outcomes, these practitioners work directly within teams to experiment, apply AI in real workflows, and demonstrate what is possible. Their impact comes from doing the work alongside teams, surfacing what works, and codifying repeatable patterns that others can adopt and scale.

• Mandatory Skills :
• AI Implementation, AI Adoption, AI Architect, Agentic AI, Generative AI, RAG, LLM.
• What you will do :
• Embed directly with teams and coach through hands-on application, working shoulder-to-shoulder with engineers, product managers, and QA to apply AI in real scenarios.
• Define and operationalize AI standards, patterns, and best practices, including usage guidelines, prompt conventions, reference architectures, and reusable templates.
• Design and deliver enablement programs, including playbooks, training materials, workshops, and hands-on coaching that translate AI concepts into practical day-to-day application.
• Integrate AI solutions into existing toolchains, such as IDEs, CI/CD pipelines, testing frameworks, and monitoring platforms, including rapid prototyping to demonstrate value.
• Influence executives and earn credibility with engineering and product teams, addressing concerns and driving adoption through visible outcomes.
• Abilities :
• Define a phased roadmap for AI adoption across the PDLC that aligns with business objectives and ties each initiative to clear value.
• Bridge product, engineering, devops, security, and compliance, ensuring AI improvements are coordinated rather than siloed.
• Redesign workflows to fully leverage AI capabilities, not simply automate existing steps.
• Drive toward measurable results while maintaining quality, safety, and compliance, and continuously refine approaches based on feedback and data.
• Experience :

10+ years of experience in software development, platform engineering, or technology consulting, with significant exposure to AI-enabled or DevOps-driven transformation initiatives.

Demonstrated success working in large, complex, and regulated enterprise environments, with hands-on experience navigating governance, security, and compliance constraints.

Prior experience acting as a change agent, program lead, consultant, or internal champion, influencing teams without formal authority and engaging both senior leaders and delivery teams.

History of building repeatable assets such as playbooks, toolkits, templates, or reference models that scale beyond individual teams and reduce dependency on ongoing staff augmentation.

Outcomes :

Tangible examples of AI-driven improvements you personally helped teams achieve, such as reduced cycle time, improved product quality, increased test coverage, or reduced operational toil.

• Proven ability to leave behind self-sustaining capabilities, not dependency, by turning experimentation into repeatable, well-documented patterns.
• Clear evidence of measurable delivery impact from prior AI initiatives.
• Track record of helping organizations adopt AI responsibly, balancing speed with risk mitigation and embedding controls rather than treating governance as a separate gate.
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