Roles & Responsibilities: 

• Lead the architecture, design, development, and enhancement of scalable, reliable, and maintainable software products and platforms. 

• Design and develop efficient, reusable, testable, modular, and scalable applications and services. 

• Design solutions for data-intensive and distributed workloads, including data extraction, processing, transformation, cleaning, enrichment, and delivery. 

• Drive product enhancements and new feature development while ensuring alignment with product objectives and long-term technical strategy. 

• Apply appropriate design patterns, object-oriented principles, data structures, algorithms, and architectural practices to solve complex engineering problems. 

• Conduct code and design reviews, promoting high standards for quality, security, performance, reliability, and maintainability. 

• Mentor engineers through technical guidance, design discussions, code reviews, knowledge sharing, and problem solving. 

• Improve engineering processes, development practices, automation, and team productivity through continuous improvement.  

Requirements: 

• 9 to 12 years of relevant work experience in software engineering and back-end application development.  

• Proven track record of delivering production-grade systems. 

• Strong hands-on experience with Python and developing RESTful APIs and backend services using FastAPI or similar frameworks. 

• Strong experience designing and implementing micro-services and distributed systems. 

• Hands-on experience with containerized application deployment. 

• Experience with SQL databases, and NoSQL databases, such as MongoDB, Opensearch/Elasticsearch including indexing, mapping, search, querying, and performance optimization. 

• Experience with data extraction, web scraping, data ingestion, cleaning, transformation, processing, and enrichment. 

• Strong understanding of API design, service-oriented architectures, integrations, and asynchronous/distributed processing. 

• Excellent communication and collaboration skills with both technical and non-technical stakeholders. 

• Good to Have: 

• Experience applying AI and Machine Learning techniques to data-related problems. 

• Exposure to LLMs, Generative AI, embedding, vector search, classification, entity matching, and automated data enrichment. 

• Experience with large-scale data platforms, distributed data processing, event-driven architectures, or data pipelines. 

• Experience contributing to technical strategy, architecture, and engineering standards across multiple teams.