Engineering Manager

Responsibilities:

• Lead and mentor a team of engineers while remaining hands-on with architecture, system design, and development.
• Architect and build scalable microservices-based platforms and AI-enabled enterprise products.
• Design and deliver high-performance, high-availability systems capable of scaling to large workloads.
• Drive development of AI-powered applications, including agentic systems, LLMs, and RAG-based solutions.
• Collaborate with product and business teams to translate requirements into robust technical solutions.
• Ensure delivery using Agile sprint processes and structured SDLC practices.
• Establish engineering best practices around code quality, testing, architecture documentation, and observability.
• Guide the team in adopting modern AI developer tools and automation to enhance productivity.
• Drive architecture decisions for data pipelines, big data platforms, and distributed systems.

Requirements:

• 10-14 years of software development experience.
• 3+ years of engineering manager / technical leadership experience.
• Strong communication, stakeholder management, and presentation skills.

Backend and Frontend:

• Strong hands-on experience with Java, Node.js, React.js, and TypeScript.

AI / LLM and Agentic Systems:

• Experience building AI-powered enterprise systems, including the development of agentic AI systems, the implementation of LLM-based solutions, and the development of RAG (Retrieval Augmented Generation) pipelines.
• Frameworks and tools such as LangChain, LLaMA, YOLO, Roboflow, and NeMo.
• Experience building production-grade AI models and integrating them into enterprise platforms.

Data Engineering and Big Data:

• Strong experience with Python, PySpark, big data ecosystems, and Hadoop.
• Experience building large-scale data processing pipelines and ML data workflows.

Databases:

• Strong experience with PostgreSQL and MySQL.
• Knowledge of database sharding, query optimization, and large-scale data storage strategies.

Distributed Systems and Messaging:

• Experience with event-driven architecture and messaging platforms: Kafka, Redis, RabbitMQ.

DevOps and Cloud:

• Hands-on experience with cloud-native infrastructure: Docker, Kubernetes, Nginx, AWS, and GCP.

Development Practices:

• Strong experience working with microservices architecture.
• Experience delivering high-scale, production-grade enterprise systems.
• Experience with Agile development methodologies.
• Familiarity with full SDLC processes: functional specs, architecture documentation, design specs, unit testing, and performance optimization.

AI Developer Productivity Tools:

• Experience leveraging modern AI-assisted development tools such as ChatGPT, Cursor, or similar.

Preferred Qualifications:

• Experience building AI-driven SaaS platforms.
• Prior experience scaling engineering teams and distributed systems.
• Strong knowledge of system reliability, scalability, and observability.
• Experience integrating AI/ML models into real-time production systems.