• Design, develop, and maintain enterprise grade conversational AI solutions across chat and voice channels • Build conversational agents using Microsoft 365 Agents SDK and Copilot Studio, following enterprise design standards • Design deterministic conversational flows with intent handling, dialog orchestration, and business-rule enforcement • Implement Generative AI fallback strategies in a controlled and compliant manner • Integrate conversational agents with backend systems using RESTful APIs (JSON) via Azure API Management (APIM) • Integrate Azure AI Speech Services (Speech-to-Text) to support voice-enabled conversational workflows • Implement WhatsApp-based conversational solutions using Twilio (BSP), including text, audio, and document handling • Collaborate closely with integration and backend teams to ensure end-to-end workflow orchestration • Support unit testing, integration testing, and UAT for conversational workflows • Optimize conversational performance through prompt tuning, context management, and flow refinement

• Own the platform for agentic engineering pods including orchestration, trust boundaries, and evaluation pipelines • Ensure collaboration between humans and AI agents is safe and effective • Coach engineers through a shift in their roles to embrace AI collaboration • Maintain engineering system integrity and quality gates for AI outputs • Collaborate with Technical Program Leader to resolve engineering flow bottlenecks

Do you want to boost your career and collaborate with expert, talented colleagues to solve and deliver against our clients' most important challenges? We are growing and are looking for people to join our team. You'll be part of an entrepreneurial, high-growth environment of 300.000 employees. Our dynamic organization allows you to work across functional business pillars, contributing your ideas, experiences, diverse thinking, and a strong mindset. Are you ready? Job Overview: Infosys Consulting is at the forefront of applied AI innovation, delivering real-world business value through the convergence of AI agents, machine learning, and modern enterprise architecture. As part of our growing Enterprise AI consulting practice, we are looking for technically hands-on professionals to design and deliver client-centric intelligent systems and support business growth through strategic pre-sales and solutioning initiatives. Key Responsibilities ·       Design and implement AI agents using LangChain, CrewAI, or AutoGen for enterprise-grade use cases. ·       Develop modular code for task decomposition, memory handling, and tool integration with APIs. ·       Collaborate with AI strategists and architects on project design and MVP delivery. ·       Conduct testing and fine-tuning of agent behavior using LLM APIs and embeddings. ·       Participate in internal code reviews, documentation, and reusable framework building. ·       Support pre-sales demos and client innovation sessions with hands-on prototypes.

About the Role This is not a rebranded Director of Engineering. It is a new position purpose-built for a new way of building software. Cover Whale is operating an agentic engineering model where pods of product managers, engineers, and AI agents deliver production software together. The Director of AI Platform Engineering owns the platform those pods operate on — the agent orchestration layer, trust boundaries, human-in-the-loop controls, and evaluation pipelines that make it safe and effective to delegate work to AI at production scale. You will be a player-coach: hands-on building the platform while also leading and developing the engineering team. Your mandate is to ensure that humans and AI agents collaborate safely, reliably, and at velocity. What You'll Own - Agentic Platform Architecture Human-in-the-loop boundaries — setting trust thresholds where AI-generated output is auto-approved versus escalated for review - Prompt and spec evaluation pipelines — the systems that validate whether AI-generated code, tests, and configurations meet production standards before reaching a human reviewer - The end-to-end specification-through-deployment pipeline: how specs become agent instructions and how agent output becomes production code - Evaluation and integration of emerging AI tooling, models, and agent frameworks Engineering System Integrity - Quality gates calibrated for AI-augmented velocity — ensuring AI-generated output meets production standards Engineering standards: branch protection, test coverage, CI/CD rules, and deployment integrity - Architectural escalation path for engineers facing technical blockers - Collaboration with the Technical Program Leader on engineering flow patterns and systemic bottlenecks People Leadership for an Agentic Team - Coaching engineers through the identity shift from "I write code" to "I orchestrate AI agents and own outcomes" Career development, performance accountability, and 1:1s — with expectations calibrated to the expanded role of delegate, review, and own - Building team culture where engineers treat AI agents as force multipliers, not threats What You Won't Own - Product direction, spec writing, or feature prioritization (that's the product management team's role) - Pod-level delivery — the pod is PM + Engineer + AI Agents; you govern the platform they operate on - Company-wide AI enablement and adoption (that's the AI Enablement Lead