Software Engineering Manager

About the Role :

We are seeking an experienced Software Engineering Manager to lead the design, development, and delivery of scalable enterprise applications combining Java engineering with modern Artificial Intelligence capabilities.

The role requires strong expertise in Java and distributed application development along with practical experience in Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and AI-powered applications.

You will lead engineering teams, drive architecture and technical decisions, and work closely with Product, Architecture, Data, and AI teams to deliver high-quality, production-ready solutions.

Key Responsibilities :

- Lead and manage software engineering teams responsible for developing scalable Java-based applications and AI-enabled product capabilities.

- Own the end-to-end engineering lifecycle including technical design, development, testing, deployment, production support, and continuous improvement.

- Drive architecture and development of enterprise applications using Java, Spring Boot, REST APIs, microservices, and distributed systems.

- Guide teams in integrating LLM capabilities into enterprise applications and customer-facing products.

- Design and implement RAG-based applications that combine enterprise data, knowledge repositories, and LLMs.

- Lead development of AI-powered features including intelligent search, document understanding, conversational applications, summarization, recommendations, and workflow automation.

- Evaluate and integrate LLM models and AI services based on product requirements, performance, security, scalability, and cost.

- Work with engineering teams to build data ingestion, chunking, embedding, retrieval, reranking, and response-generation workflows for RAG solutions.

- Drive integration of vector databases and semantic search capabilities with Java-based applications.

- Establish engineering standards for AI application development, prompt management, evaluation, observability, security, and responsible AI practices.

- Collaborate with Data Scientists, AI Engineers, Architects, Product Managers, and Business stakeholders to translate requirements into scalable solutions.

- Review architecture and technical designs and ensure alignment with enterprise engineering standards.

- Mentor engineers, conduct technical reviews, identify skill gaps, and support team development.

- Drive Agile engineering practices, sprint planning, estimation, delivery tracking, and continuous improvement.

- Ensure applications meet requirements for scalability, reliability, security, performance, and maintainability.

- Monitor production systems, identify technical risks, and drive resolution of complex engineering issues.

- Participate in technology evaluation and contribute to the organization's AI and application modernization roadmap.

Java & Application Engineering:

- Strong experience with Java and enterprise application development.

- Hands-on experience with Spring Boot, Spring Framework, REST APIs, microservices, and distributed systems.

- Experience with relational and NoSQL databases and data-access technologies.

- Understanding of asynchronous processing, messaging, caching, API gateways, and event-driven architectures.

- Experience designing high-performance and highly available backend systems.

AI, LLM & RAG:

- Practical experience integrating LLMs into enterprise or product applications.

- Strong understanding of RAG architecture and its components, including document ingestion, preprocessing, embeddings, vector search, retrieval, reranking, and response generation.

- Experience working with vector databases and semantic search technologies.

- Understanding of prompt engineering, LLM evaluation, context management, hallucination mitigation, and response quality optimization.

- Experience integrating AI services through APIs and building AI-enabled workflows.

- Knowledge of Agentic AI, AI agents, tool calling, function calling, and multi-step AI workflows is an advantage.

- Experience with Python for AI-related integrations or data processing is desirable.

Cloud & DevOps:

- Experience deploying Java and AI-enabled applications on AWS, Azure, or GCP.

- Good understanding of Docker, Kubernetes, CI/CD, monitoring, logging, and application observability.

- Experience with cloud-native architecture and scalable deployment patterns.

Leadership & Stakeholder Management:

- Work closely with Product Managers and senior stakeholders to define technical priorities and delivery roadmaps.

- Translate business requirements into technical solutions and measurable engineering outcomes.

- Communicate architecture, technical risks, delivery status, and technology recommendations to senior leadership.

- Build a culture of engineering excellence, accountability, collaboration, and innovation.

Required Experience & Skills:

- 10 - 15 years of experience in software engineering and enterprise application development.

- Strong expertise in Java and Spring Boot.

- Experience leading engineering teams and delivering complex software products.

- Hands-on experience with LLM, Generative AI, and RAG-based applications.

- Strong understanding of microservices, APIs, distributed systems, and cloud-native development.

- Experience with vector databases, embeddings, semantic search, and AI integration patterns.

- Strong problem-solving and system-design capabilities.

- Excellent communication, stakeholder management, mentoring, and people leadership skills.

Preferred Experience:

- Experience building AI-enabled enterprise products in a product engineering environment.

- Exposure to Agentic AI, AI orchestration frameworks, or LLM application frameworks.

- Experience with technologies such as Kafka, Redis, Elasticsearch/OpenSearch, or equivalent distributed technologies.

- Experience with MLOps, AI observability, model evaluation, and AI governance.

Qualifications:

- Bachelor's or Master's degree in Computer Science, Engineering, Information Technology, or a related discipline.

Skills

Java, Spring Boot, Artificial Intelligence, Machine Learning, Cloud Computing, Microservices Architecture, Generative AI, Distributed Systems, LLM, RAG