Senior Manager, AI Engineering

Job Description

Job Title : Senior Manager - AI Engineering

Location : Hyderabad

Years of Experience : 10+ years in software engineering and AI/ML development, with 4+ years in engineering leadership or management roles.

Position Overview

We are seeking an experienced and visionary Senior Manager - AI Engineering to lead high-performing AI engineering teams responsible for building, deploying, and scaling AI-powered products and platforms. This leader will drive the engineering execution of Generative AI, LLM-based applications, AI platforms, intelligent automation, and machine learning solutions that directly power customer experiences and business outcomes.

The ideal candidate combines strong engineering fundamentals with hands-on expertise in modern AI technologies, cloud-native architectures, and large-scale distributed systems. They should have proven experience building production-grade AI solutions while developing high-performing engineering teams in a product-driven organization.

In this role you will...

Team Leadership and Development

Lead, mentor, and grow a team of AI engineers, machine learning engineers, and software engineers.

Foster a culture of innovation, experimentation, engineering excellence, and continuous learning.

Build organizational capability in Generative AI, LLM engineering, AI platforms, and modern software engineering practices.

Drive technical leadership, career development, and succession planning across the organization.

AI Engineering & Technical Leadership

Drive the architecture, design, and development of scalable AI-powered products using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, and machine learning solutions.

Lead engineering efforts across Python, cloud-native technologies, APIs, vector databases, orchestration frameworks, and distributed systems.

Define AI engineering standards, reusable frameworks, governance, and best practices for secure and reliable AI application development.

Conduct architecture, design, and code reviews to ensure quality, scalability, maintainability, and security.

Product Delivery

Partner closely with Product Management, Data Science, UX, and Platform Engineering teams to define AI product roadmaps and technical strategy.

Translate business opportunities into scalable AI capabilities and production-ready solutions. Lead sprint planning, technical execution, prioritization, and resource allocation to deliver high-quality AI products on schedule.

Drive rapid experimentation while ensuring production readiness and long-term maintainability.

AI Platform & Operational Excellence

Build and scale enterprise AI platforms supporting model serving, prompt management, RAG pipelines, AI observability, evaluation frameworks, and MLOps.

Implement engineering best practices including CI/CD, Infrastructure as Code, automated testing, monitoring, and AI model lifecycle management.

Ensure AI systems are secure, reliable, scalable, cost-efficient, and compliant with responsible AI principles.

Establish engineering metrics around model quality, latency, cost optimization, reliability, and customer impact.

Stakeholder Engagement

Serve as the bridge between engineering leadership, product teams, executive stakeholders, and business partners.

Communicate technical strategy, architecture decisions, delivery progress, risks, and business outcomes.

Influence AI adoption across the organization by aligning engineering initiatives with strategic business priorities.

You've got what it takes if you have...

AI & Technical Expertise

Strong hands-on experience developing production-grade AI applications using Java, Python and modern AI frameworks.

Deep expertise with Large Language Models (LLMs), Generative AI, Retrieval-Augmented Generation (RAG), prompt engineering, AI agents, embeddings, and vector databases.

Experience integrating commercial and open-source foundation models through APIs and model serving platforms.

Strong understanding of AI orchestration frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, or similar technologies.

Experience designing scalable microservices, REST APIs, event-driven architectures, and cloud-native AI applications.

Expertise with cloud platforms such as AWS, Azure, or Google Cloud for deploying AI workloads. Experience building AI platforms leveraging services such as Kubernetes, Docker, serverless technologies, model hosting platforms, and GPU-enabled infrastructure.

Practical experience with MLOps practices including model deployment, versioning, monitoring, experimentation, evaluation, and continuous improvement.

Strong knowledge of SQL, NoSQL databases, vector databases, caching strategies, and performance optimization for AI workloads.

Understanding of AI governance, model security, privacy, responsible AI, and enterprise AI architecture.

Ability to evaluate technical trade-offs balancing accuracy, latency, scalability, security, cost, and business value.

Leadership Experience

Proven experience leading AI engineering or software engineering teams within a product organization.

Demonstrated ability to recruit, mentor, and retain high-performing engineering talent.

Experience managing multiple engineering initiatives while maintaining technical excellence and delivery predictability.

Strong track record of driving engineering transformation and adoption of emerging AI technologies.

Product Mindset

Deep understanding of the end-to-end AI product lifecycle from ideation, experimentation, and validation through production deployment and continuous optimization.

Experience working cross-functionally with Product Management, Data Science, Design, and Customer Success teams to deliver customer-centric AI solutions.

Ability to prioritize engineering investments based on measurable customer and business impact.

Operational Skills

Strong experience with Agile methodologies, sprint planning, engineering execution, and resource management.

Hands-on knowledge of DevOps, MLOps, CI/CD pipelines, Infrastructure as Code, cloud automation, and AI observability.

Experience establishing engineering KPIs, operational metrics, and delivery governance for AI products and platforms.

Communication

Excellent written and verbal communication skills with the ability to communicate complex AI concepts to technical and non-technical audiences.

Ability to influence senior leadership through technical vision, data-driven decision making, and strategic execution.

Preferred Qualifications

Experience delivering enterprise-scale Generative AI products in production.

Familiarity with foundation models from OpenAI, Anthropic, Google, Meta, or open-source ecosystems.

Experience with vector databases such as Pinecone, Weaviate, Milvus, pgvector, or similar technologies.

Exposure to AI evaluation frameworks, LLMOps, AI safety, and responsible AI practices.

Experience leading platform engineering initiatives supporting AI at scale.

Master's degree in computer science, Artificial Intelligence, Machine Learning, or a related field is preferred.

Why Join Us?

Innovative Environment
Be part of an organization building next-generation AI-powered products that redefine customer experiences.

Career Growth
Lead strategic AI engineering initiatives with opportunities to shape technology direction and organizational capability.

Collaborative Culture
Work alongside world-class engineers, data scientists, product leaders, and AI practitioners committed to delivering exceptional products.

Core Values
Join a company that lives by the values of shattering boundaries, sparking greatness, and sharing success in everything we do.

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