You will:

• Lead team level system design, including high level design, detailed design, API contracts, data flows, service interactions, integration patterns, and nonfunctional requirements such as scalability, reliability, performance, security, and operability.
• Partner with lead engineers and architects to align team level implementations with broader platform architecture, modernization goals, engineering standards, and long term maintainability.
• Develop and optimize data intensive applications and analytics capabilities using Databricks, Spark, and related technologies to process and deliver data at scale.
• Develop, and deploy AI-powered products, intelligent agents, and agentic workflows utilizing Large Language Models (LLMs), retrieval systems, orchestration frameworks, tool-calling patterns, and context-aware workflows.
• Implement advanced agentic systems incorporating planning, reasoning loops, memory management, grounding, evaluation frameworks, safety mechanisms, and human-in-the-loop controls.
• Identify and resolve performance bottlenecks across services, APIs, data processing workloads, and user experiences.
• Collaborate with cross-functional teams to translate business requirements into scalable technical solutions.
• Mentor junior and mid-level engineers through technical guidance, pairing, code reviews, design feedback, documentation, and knowledge sharing.
• Contribute to a collaborative engineering culture by modelling ownership, clear communication, continuous learning, pragmatic decision-making, and high standards for software quality.
• Improve engineering quality through code reviews, design reviews, automated testing, secure coding practices, observability, performance tuning, CI/CD practices, and production readiness.
• Troubleshoot complex issues across application, service, API, data, and cloud layers, driving root cause analysis and implementing durable solutions that improve platform reliability and supportability.

Ideal qualifications include:

• 6+ years of software engineering experience delivering scalable, high-performance, and reliable software in an agile environment.
• Strong hands-on experience designing and building distributed systems, microservices, RESTful APIs, and cloud native applications.
• Strong programming experience with modern engineering stacks such as Java/Spring Boot, TypeScript/React, SQL, and related frameworks and technologies.
• Experience with Python, Databricks, Spark, or PySpark is highly desirable, particularly for working with data pipelines and analytics-driven applications.
• Experience building or supporting cloud native applications on AWS and/or Azure, with familiarity in CI/CD, containerization, Kubernetes, observability, automated testing, and production support practices.
• Demonstrated ability to lead team-level design discussions, evaluate technical trade-offs, and guide implementation through technical influence.
• Ability to translate product and business needs into scalable, secure, and maintainable technical solutions.
• Strong collaboration and communication skills, with the ability to work effectively across cross-functional teams.
• Strong problem solving skills with the ability to work through ambiguity, make sound technical decisions, and drive results through hands on execution.
• Bachelor’s degree in Computer Science, Software Engineering, or a related technical field, or equivalent practical experience; an advanced degree is a plus.

Essential skills/knowledge/experience: (Up to 10, Avoid repetition)

• Full stack or backend software development - Mandatory
• Java and Spring Boot – Mandatory
• Python / PySpark - Mandatory
• TypeScript and React where applicable
• Microservices and RESTful APIs - Mandatory
• Relational databases and distributed data stores - Mandatory

Desirable skills/knowledge/experience: (As applicable)

• AWS and Azure cloud platforms – Nice to have
• Automated testing, CI/CD, observability, and production readiness – Nice to have
• Data-intensive and analytics-driven applications – Nice to have
• Databricks, Spark experience – Nice to have
• Security, scalability, reliability, performance, and maintainability – Nice to have.
•  

You will:

• Lead team level system design, including high level design, detailed design, API contracts, data flows, service interactions, integration patterns, and nonfunctional requirements such as scalability, reliability, performance, security, and operability.
• Partner with lead engineers and architects to align team level implementations with broader platform architecture, modernization goals, engineering standards, and long term maintainability.
• Develop and optimize data intensive applications and analytics capabilities using Databricks, Spark, and related technologies to process and deliver data at scale.
• Develop, and deploy AI-powered products, intelligent agents, and agentic workflows utilizing Large Language Models (LLMs), retrieval systems, orchestration frameworks, tool-calling patterns, and context-aware workflows.
• Implement advanced agentic systems incorporating planning, reasoning loops, memory management, grounding, evaluation frameworks, safety mechanisms, and human-in-the-loop controls.
• Identify and resolve performance bottlenecks across services, APIs, data processing workloads, and user experiences.
• Collaborate with cross-functional teams to translate business requirements into scalable technical solutions.
• Mentor junior and mid-level engineers through technical guidance, pairing, code reviews, design feedback, documentation, and knowledge sharing.
• Contribute to a collaborative engineering culture by modelling ownership, clear communication, continuous learning, pragmatic decision-making, and high standards for software quality.
• Improve engineering quality through code reviews, design reviews, automated testing, secure coding practices, observability, performance tuning, CI/CD practices, and production readiness.
• Troubleshoot complex issues across application, service, API, data, and cloud layers, driving root cause analysis and implementing durable solutions that improve platform reliability and supportability.

Ideal qualifications include:

• 6+ years of software engineering experience delivering scalable, high-performance, and reliable software in an agile environment.
• Strong hands-on experience designing and building distributed systems, microservices, RESTful APIs, and cloud native applications.
• Strong programming experience with modern engineering stacks such as Java/Spring Boot, TypeScript/React, SQL, and related frameworks and technologies.
• Experience with Python, Databricks, Spark, or PySpark is highly desirable, particularly for working with data pipelines and analytics-driven applications.
• Experience building or supporting cloud native applications on AWS and/or Azure, with familiarity in CI/CD, containerization, Kubernetes, observability, automated testing, and production support practices.
• Demonstrated ability to lead team-level design discussions, evaluate technical trade-offs, and guide implementation through technical influence.
• Ability to translate product and business needs into scalable, secure, and maintainable technical solutions.
• Strong collaboration and communication skills, with the ability to work effectively across cross-functional teams.
• Strong problem solving skills with the ability to work through ambiguity, make sound technical decisions, and drive results through hands on execution.
• Bachelor’s degree in Computer Science, Software Engineering, or a related technical field, or equivalent practical experience; an advanced degree is a plus.

Essential skills/knowledge/experience: (Up to 10, Avoid repetition)

• Full stack or backend software development - Mandatory
• Java and Spring Boot – Mandatory
• Python / PySpark - Mandatory
• TypeScript and React where applicable
• Microservices and RESTful APIs - Mandatory
• Relational databases and distributed data stores - Mandatory

Desirable skills/knowledge/experience: (As applicable)

• AWS and Azure cloud platforms – Nice to have
• Automated testing, CI/CD, observability, and production readiness – Nice to have
• Data-intensive and analytics-driven applications – Nice to have
• Databricks, Spark experience – Nice to have
• Security, scalability, reliability, performance, and maintainability – Nice to have.
•