Senior Engineering Manager- Infrastructure AI, Automation & Tools

Position Title :

Sr Engineering Manager – Infrastructure AI, Automation & Tools

Position Location : Gurgaon

Requisition ID : 39505

Our Global Technology team’s goal is to leverage technology and data to drive profitable growth, focus on enhancing customer experience and to further our purpose of ' Celebrating real connections through delicious, planet-friendly food '. McCain has embarked on an ambitious digital transformation across our business from Agriculture to Manufacturing and commercial capabilities to enhance our Customer obsession. As part of this transformation, we are making significant investments into our digital platforms, technology transformations and in building a data driven culture. We are building digital products for our customers, suppliers / growers, and McCain team members to enable digital processes and data-driven automation. Through our investments, we will transform McCain into a company that empowers our teams with easy-to-use systems which will help them collaborate better, be productive and make data driven decisions. Will you be part of this exciting journey?

Our Global Technology team’s goal is to inspire growth through innovative use of technology and data. McCain has embarked on an ambitious digital transformation across our business from Agriculture, Manufacturing, Supply Chain to how we serve our customer and consumers.

Overview

McCain seeks a visionary, hands-on Senior Engineering Manager to lead our Infrastructure AI, Automation & Tools team. This pivotal role blends the latest in Agentic AI, cloud-native platforms, automation, and advanced developer tooling. You will be responsible for establishing the intelligent automation backbone for McCain’s digital transformation, focusing on resilient global infrastructure and seamless integration with business processes.

The Sr Engineering Manager - Infrastructure AI, Automation & Tools, is a critical hands-on technical Enginnering leadership role in driving McCain’s 2030 strategy, enabling digital transformation and modernization of Infrastructure Engineering with AI First, Automation first mindset across the enterprise.

This Engineering leader will transform global IT infrastructure services with GenAI, and Agentic AI. This role require deep hands-on skills and not a people manager role, however they will have a team of bright engineers from IITs / BITS working with them.

This role will embed AI / ML / GenAI / Agentic AI first automation approach in Infrastructure transformation tracks, Cloud, DevOps, Site Reliability Engineering (SRE), observability, AIOps, and Service Now areas to bring autonomous Engineering into the DNA of Infrastructure Engineering and Operations . A strong AI and Automation first, background is essential.

Key Responsibilities

Strategic Technical Leadership :

Provide technical direction and leadership to a cross-functional engineering team specializing in Infrastructure AI, cloud solutions, automated operations, and developer tooling. Cultivate a high-performance, innovation-driven culture.

Agentic AI Enablement :

Architect, deploy, and scale Agentic AI systems that perform adaptive, autonomous operations—including multi-agent coordination, intelligent decision-making, and proactive remediation—across cloud and hybrid environments.

Developer Tools Innovation :

Lead the creation and integration of intelligent developer tools (e.g., code assistants, automated testing, observability integrations) to increase engineering productivity and software reliability.

ITSM & DevOps Integration :

Engineer seamless, automated integrations between ServiceNow (ITSM / ITOM) and Jira (Agile / DevOps), enabling bi-directional workflow automation for incident, change, and problem management. Ensure traceability, auditability, and governance across digital operations.

AI Orchestration & Event Automation :

Implement and champion the adoption of AI orchestration tools (such as Airflow, Kubeflow, Azure ML Pipelines) to automate, monitor, and manage distributed agent workflows, data pipelines, and ML operations. Integrate orchestration platforms with observability and incident management for true event-driven automation.

GenAI & Agentic AI–Driven Autonomous Operations :

Deploy autonomous IT operations powered by GenAI and Agentic AI across all infrastructure domains. Champion and partner with vendors and internal teams to accelerate automation of IT operations using GenAI and operational Agentic agents. Integrate GenAI into ServiceNow ITSM for automated incident, request, and change workflows. Apply AI-driven automation to network operations for predictive performance, anomaly detection, and self-healing.

Collaboration & Stakeholder Engagement :

Partner closely with cybersecurity, data science, enterprise architecture, operations, and business leaders to ensure AI and automation initiatives are business-aligned, secure, and deliver tangible value.

Program Management Rigor :

Apply rigorous project management discipline—enforcing stage gates, entry / exit criteria, and structured feedback—to ensure high-quality delivery and continuous improvement across multiple parallel initiatives. Build a culture of automation-first, AI-augmented, and customer-centric service delivery.

Establish strategic partnerships with vendors focused on GenAI, Agentic AI, and next-generation automation.

Key Technical Skills & Experience

Agent-oriented programming, multi-agent system design, and agent orchestration frameworks

(BDI architectures, event-driven agent models)

Reinforcement learning, generative AI models (LLMs),

causal / predictive modeling for autonomous infrastructure optimization

Advanced proficiency with AI orchestration and workflow

tooling (Airflow, Argo, Prefect, Azure ML Pipelines, Kubeflow)

Delivering Agentic AI solutions in production

(multi-agent collaboration, automated remediation, autonomous diagnostics)

Programming Expertise :

Advanced proficiency in Python (preferred) or Go, with working knowledge of Java and / or C++ for scalable AI agent and LLM / SLM system development.

AI / ML Model Development :

Deep understanding of machine learning fundamentals, including supervised and unsupervised learning, model training, tuning, and evaluation.

Expertise in developing, deploying, and fine-tuning LLMs / SLMs

using modern frameworks (e.g., PyTorch, TensorFlow, Hugging Face Transformers).

Experience with prompt engineering, retrieval-augmented generation (RAG),

model quantization, and optimization for resource-constrained deployments.

Natural Language Processing (NLP) : Hands-on experience with tokenization, entity recognition, text classification, semantic search, and conversational AI system design. Familiarity with leading NLP libraries (spaCy, NLTK, Hugging Face).

Agentic AI & Orchestration : Demonstrated ability to design and build agent-based solutions, including agent workflow planning, multi-agent coordination, and memory management. Proficient in orchestration platforms / frameworks such as LangChain, CrewAI, AutoGen, RooCode, or similar for managing agentic workflows and task automation.

Experience developing knowledge graphs, ontologies , and reasoning algorithms to support agent autonomy and explainability.

Familiarity with chain-of-thought prompting, error recovery, multi-agent collaboration, and emerging protocols (MCP, A2A) for agentic communication.

Commitment to staying current with industry trends (via research, courses, developer blogs), and to thorough documentation for reproducibility and knowledge sharing.

Experience with Git-based version control, GitHub CI / CD automation, and responsible AI practices.

Integrating ITSM (ServiceNow) with DevOps (Jira) and operational event feeds for real-time, closed-loop automation. Expert in ServiceNow-Jira integrations for automated ITSM and DevOps workflows, leveraging platforms such as IntegrationHub, Exalate, custom API / webhook solutions

Experience in Azure DevOps to Jira Plugins and integrations

Deploying and scaling AI orchestration tools for enterprise ML / Ops, agent scheduling, or large data pipeline automation

Qualifications

Bachelor’s or Master’s in Data Science, AI or Machine Learning, or related field.

10+ years of IT infrastructure, Platform Engineering, with at least 3 years in a AI Engineering role

Certification in AI and Agentic AI is huge plus

Executive Ready communications skills with ability to present at VP and CXO level