Senior Solution Engineer Java | AWS | Snowflake | AI/GenAI Company: Scholastika Knowledge Base Pvt. Ltd. Client: Straive Job Location: Mumbai, Maharashtra Work Mode: Hybrid – 3 days per week in office Employment Type: Full-time, Permanent Experience: 6–16 years preferred Notice Period: Immediate to 60 days Openings: 1 Tentative Compensation: 2.5–3 lakh per month, depending on current CTC, experience and technical fit Job Overview We are looking for a hands-on Senior Solution Engineer with strong expertise across Java engineering, AWS cloud architecture, Snowflake, data engineering and AI/GenAI integration. This is a technically demanding engineering role for someone who can design, build, deploy and own production-grade applications and data solutions—not simply coordinate development or work on isolated application features. The ideal candidate combines strong Java/Spring Boot engineering with practical AWS production experience, Snowflake-based data processing, AI/GenAI integration and modern AI-assisted engineering practices. Candidates must demonstrate the ability to make technical decisions, troubleshoot production issues, optimise systems and deliver reliable solutions across application and data layers. Key Responsibilities 1. Java Application Engineering Design, develop and maintain enterprise-grade applications using Java, Spring Boot and RESTful APIs. Build scalable, secure and maintainable backend services. Apply sound object-oriented design, exception handling, automated testing and performance optimisation. Diagnose complex application issues and take ownership of production defects. 2. AWS Cloud Engineering Design, deploy and support production workloads on AWS. Demonstrate practical knowledge of AWS architecture, IAM, networking, security, monitoring and observability. Troubleshoot cloud infrastructure, application deployment and runtime issues. Make informed decisions about reliability, scalability, performance and cost optimisation. 3. Snowflake and Data Engineering Develop and optimise Snowflake SQL queries, data models and data processing workflows. Work with data ingestion, transformation, integration and processing pipelines. Handle complex SQL, performance tuning, data validation and troubleshooting. Apply appropriate practices for data access, governance, reliability and efficient processing. 4. AI/GenAI Engineering Integrate AI/GenAI capabilities into applications and enterprise workflows. Work with AI services, APIs and relevant application integration patterns. Understand practical implementation considerations such as response quality, latency, security, reliability and evaluation. Contribute to production-ready AI-enabled solutions rather than limiting work to demonstrations or prototypes. 5. AI-Assisted Software Engineering Use AI-assisted development tools such as GitHub Copilot, Cursor or Claude to improve engineering productivity. Apply these tools to code generation, refactoring, debugging, testing and documentation. Review and validate AI-generated code for correctness, security, performance and maintainability. Demonstrate responsible use of AI tools within an established software engineering workflow. 6. CI/CD, Infrastructure as Code and Production Ownership Build and maintain automated CI/CD pipelines. Work with Infrastructure as Code (IaC) and repeatable deployment practices. Support automated testing, release management, observability and production troubleshooting. Perform root-cause analysis and implement permanent corrective actions. Take end-to-end ownership of delivered solutions, including operational stability and ongoing improvements. Mandatory Technical Requirements — Hard Screening Candidates must demonstrate practical, hands-on experience in the following areas: 1. Java and Backend Engineering Strong Java development experience, preferably with Spring Boot and REST APIs. Ability to explain application architecture, design decisions, debugging and production troubleshooting. 2. AWS Production Depth Real-world experience deploying and supporting production workloads on AWS. Practical understanding of architecture, IAM, security, networking, monitoring and troubleshooting. Basic AWS familiarity or certification without production experience is insufficient. 3. Snowflake Hands-on experience with Snowflake, including SQL, data processing, modelling or pipeline integration. Ability to explain actual project contributions and data-related engineering challenges. 4. AI/GenAI Practical experience integrating AI/GenAI into applications or workflows. Ability to explain the implementation, integration approach and production considerations. 5. AI-Assisted Engineering Practical use of AI-assisted coding tools such as Claude, Cursor or GitHub Copilot. Ability to explain how these tools were used in actual engineering work and how generated code was validated. 6. CI/CD and IaC Hands-on experience with automated build, test and deployment pipelines. Practical exposure to Infrastructure as Code and repeatable deployment processes. 7. Data Engineering Experience working with SQL, data integration, data transformation or data processing pipelines. Ability to troubleshoot data quality, processing or performance issues. 8. Production Ownership Demonstrable responsibility for applications or workloads running in production. Experience with monitoring, incident resolution, root-cause analysis, reliability and performance improvement. Important: These requirements are intended as hard technical gates. Candidates with experience limited to Java development alone, AWS certification alone, basic Snowflake exposure, AI experimentation without implementation, or development without production ownership may not be shortlisted. Soft Screening Requirements Candidates should also meet the following role and working requirements: Comfortable working from the Mumbai office 3 days per week . Available for a full-time, permanent position. Notice period between immediate and 60 days. Able to communicate technical decisions, implementation details and production issues clearly. Comfortable working independently and collaborating with application, data and infrastructure teams. Able to explain personal contributions to projects rather than relying solely on team-level accomplishments. Willing to participate in a detailed technical evaluation covering the mandatory skills. Good-to-Have Skills The following strengthen a candidate's profile but do not replace the mandatory requirements: AWS Bedrock or other managed AI services. Retrieval-Augmented Generation (RAG), AI agents and LLM application patterns. AI evaluation, guardrails and responsible AI implementation. Advanced Snowflake optimisation, data governance and data-sharing practices. Advanced AWS architecture, observability and cost optimisation. Advanced Java/Spring Boot architecture and distributed systems. Strong automated testing, secure coding and application performance engineering. Experience delivering AI-enabled, data-intensive enterprise applications. Experience and Eligibility Candidates with 6–16 years of experience are preferred. Candidates with 4.5 years or more may be considered as an exception only if they demonstrate strong evidence across all mandatory technical requirements . Meeting the minimum experience threshold does not automatically qualify a candidate. Application Instructions Please apply with an updated CV that clearly highlights: Java/Spring Boot project experience. Production AWS architecture and troubleshooting experience. Snowflake implementation and data engineering contributions. AI/GenAI integration projects. Practical use of AI-assisted development tools. CI/CD, Infrastructure as Code and production ownership. Current location, notice period and willingness to work in Mumbai under the hybrid arrangement. Candidates should be prepared to discuss specific projects, architecture decisions, technical challenges and their individual contributions during screening. Only candidates who meet the mandatory technical requirements and can demonstrate hands-on engineering depth will be shortlisted.