Company Description SysTechCorp Inc delivers leading-edge technology services that help clients achieve and surpass their strategic business goals. The organization focuses on AI, machine learning, mobile, cloud, and ERP solutions, combining advanced tools with practical implementation expertise.

Its teams draw on years of experience and deep domain knowledge across multiple industry verticals, enabling tailored solutions for diverse business environments. Potential applicants will join a company that values innovation, technical excellence, and long-term client partnerships.

Role Description THE OPPORTUNITY

We are building a new service to collect and normalize data catalogs from diverse data sources — including relational databases, data lakes, data warehouses, and streaming systems — and expose them to an AI agent that dynamically constructs and routes queries to the appropriate source. This is a greenfield initiative that requires strong engineering judgment, a systems-thinking mindset, and experience shipping production-grade services.

You will be a core contributor on this project, working from architecture to implementation — designing ingestion pipelines, building the catalog API layer, and collaborating with the AI/ML team to surface the right metadata signals for intelligent query generation.

RESPONSIBILITIES

- Design, build, and operate a highly available data catalog collection service that ingests schema and metadata from heterogeneous data sources (RDBMS, data lakes, streaming platforms, APIs)
- Develop robust data pipelines for catalog extraction, normalization, lineage tracking, and semantic tagging to power AI-driven query routing
- Build and maintain RESTful and/or gRPC APIs that expose catalog data to an AI query agent




- Deploy and manage services on Kubernetes (K8s), including helm chart authoring, autoscaling configuration, and multi-cluster operations
- Ensure service reliability through SLO definition, circuit breakers, retry logic, and distributed tracing
- Integrate with open-source and cloud-native technologies including Apache Kafka, Spark, dbt, Apache Atlas, or OpenMetadata
- Collaborate with AI/ML engineers to design and iterate on the metadata schema and query routing interface
- Participate in on-call rotations and contribute to incident response, postmortems, and reliability improvements
- Contribute to CICD pipelines, infrastructure-as-code (Terraform / Helm), and automated testing frameworks

QUALIFICATIONS

Required

- 3+ years of software engineering experience building and operating production services
- Proficiency in one or more of: Go, Rust, Java or Python-- with a preference for Rust or Python for backend services
- Hands-on experience with data pipeline development: ingestion, transformation, and metadata management at scale
- Solid understanding of RESTful API design principles and service-to-service communication patterns
- Experience deploying and operating services on Kubernetes (K8s) in production cloud environments
- Familiarity with at least one major public cloud platform: AWS, Azure, or GCP




- Solid knowledge of relational and non-relational database systems and their schema/catalog semantics
- Experience with distributed messaging systems such as Apache Kafka or AWS Kinesis
- Proficiency with Git, code review workflows, and agile development practices
- Excellent troubleshooting skills and comfort operating in Linux environments

Preferred

- Experience with data catalog or metadata management tools such as Apache Atlas, Open Metadata, DataHub, or Collibra
- Familiarity with semantic search, vector databases, or LLM-based query generation systems
- Experience designing or integrating AI/ML model APIs into production backend services
- Knowledge of data governance, lineage tracking, and schema registry patterns
- Experience with infrastructure-as-code tools: Terraform, Pulumi, or AWS CDK
- Background in multi-tenant SaaS platform engineering
- Contributions to open-source data or infrastructure projects
- Go, Rust, Java or Python-- with a preference for Rust or Python for backend services
- Hands-on experience with data pipeline development: ingestion, transformation, and metadata management at scale
- Solid understanding of RESTful API design principles and service-to-service communication patterns
- Experience deploying and operating services on Kubernetes (K8s) in production cloud environments
- Familiarity with at least one major public cloud platform: AWS, Azure, or GCP
- Strong knowledge of relational and non-relational database systems and their schema/catalog semantics
- Experience with distributed messaging systems such as Apache Kafka or AWS Kinesis
- Proficiency with Git, code review workflows, and agile development practices
- Excellent troubleshooting skills and comfort operating in Linux environments

📌 Backend Engineer Data Platform And AI Infrastructure (India)
🏢 SysTechCorp
📍 India