About AquilaClouds
AquilaClouds is a Silicon Valley-based startup revolutionizing AI-powered cloud management. We’re building the next generation of autonomous cloud platforms—driven by AI and intelligent automation—that self-optimize, proactively protect, and seamlessly migrate workloads across on-premises, multi-cloud, and containerized environments.
We foster a collaborative culture focused on collective growth, delivering exceptional value to our customers, and tackling ambitious challenges. We believe in asking tough questions, supporting each other with critical thinking and innovative solutions, and winning together.
If you’re ready to join a team driving exponential innovation and growth, let’s talk.
About the role
This role covers two halves of the same problem, and we mean both of them seriously.
The first is optimization and recommendations — the product surface customers actually pay us for. Ingesting billing data is table stakes; what matters is the answer to “what should I do about it?” You’ll own recommendation engines that identify rightsizing opportunities, idle and orphaned resources, commitment (RI/Savings Plan/CUD) coverage gaps, storage tiering wins and Kubernetes waste, quantify the savings, and track whether the customer realized them.
The second is the analytics platform those recommendations and reports run on. Every optimization insight and every dashboard in Aquila resolves to an aggregation query over hundreds of millions of rows of partitioned billing data. Keeping those queries fast — and keeping them fast as customer estates grow — is not maintenance work here, it’s a first-class engineering track with its own roadmap.
We pair these deliberately. Engineers who only build recommendation logic tend to ship features that fall over on a large enterprise estate; engineers who only tune queries lose sight of what the number is for. We want someone who does both, and who can tell which of the two a given problem actually is.
Responsibilities and Duties
Optimization & recommendations
Architecture & Engineering Design
- Architect & Lead Recommendation Engines: Architect, scale, and own core recommendation microservices end-to-end, governing data ingestion algorithms, savings models, API surface areas, and accuracy metrics for customer deliverables.
- Develop Agentic AI solutions: Agents continuously monitoring and optimizing workloads.
- Design Workload Rightsizing Services: Design advanced rightsizing models across compute, managed databases, App Service Plans, storage, and Kubernetes workloads, engineering proprietary classification logic when provider-native recommendations fall short.
- Engineer High-Scale Analytical Schemas: Define schema design, partitioning strategies, normalization tiers, and indexing architecture for partitioned PostgreSQL data models processing hundreds of millions of billing rows.
- Build High-Performance Microservices: Develop high-throughput Python/FastAPI microservices alongside core Java/Spring Boot platforms, directing deployment and operational readiness across Linux VMs and Kubernetes environments.
System Reliability & Analytics Platform
- Drive Analytical Query Performance: Drive performance optimization across analytical query layers, tuning aggregation pipelines, materialized views, temporary table workflows, and covering indexes to guarantee sub-second dashboard response times.
- Diagnose & Resolve Database Bottlenecks: Diagnose root-cause database bottlenecks using EXPLAIN (ANALYZE, BUFFERS), addressing partition pruning failures, work_mem allocations, bloat remediation, and OOM pressure under concurrent workloads.
- Model Telemetry & Commitment Analytics: Process complex telemetry from AWS CloudWatch, Azure Monitor, GCP, and OCI into actionable sizing heuristics while building commitment analytics (RI, Savings Plans, CUDs) against real usage trends.
- Evaluate Next-Gen Storage Technologies: Evaluate and integrate emerging storage and execution frameworks, including columnar formats, partition archive strategies, and analytical sidecars (e.g., ClickHouse, DuckDB) where appropriate.
Collaboration & Business Impact
- Quantify Realized Savings: Establish robust validation frameworks to measure realized cost savings post-recommendation acceptance, building a clear feedback loop for customer value verification.
- Partner with Product & Customer Success: Partner directly with Product and Customer Success teams on enterprise POCs, prospective client readouts, and technical strategy sessions, translating client requirements into platform features.
- Champion Engineering Best Practices: Set engineering standards for data access layers in Spring Boot, leading code reviews, establishing regression benchmarks, and preventing inefficient ORM query generation.
Skills
- Experience: 6-10+ years of hands-on experience building and delivering enterprise-grade backend systems and high-throughput data platforms.
- PostgreSQL Expertise: Deep proficiency with advanced PostgreSQL (execution plan analysis, query tuning over 100M+ rows, table partitioning, materialized views, work_mem tuning, autovacuum management, and bloat remediation).
- Backend Languages & Frameworks: Strong Java/Spring Boot (REST APIs, JSON, JPA/Hibernate, native SQL) and Python (FastAPI/Flask, SQLAlchemy).
- Cloud Ecosystems & Telemetry: Proven experience with cloud provider APIs across AWS, Azure, GCP, or OCI (Advisor/Trusted Advisor, CloudWatch/Monitor, Pricing and Cost APIs).
- FinOps & Cost Mechanics: Solid grasp of cloud pricing models (on-demand vs. commitment programs, amortization, blended/unblended costs, storage tiering).
- Data Infrastructure & Analytical Engines: Familiarity with analytical data stores and tools (Databricks, Spark, ClickHouse, DuckDB, columnar formats, Kubernetes observability) is desirable.
- Soft Skills & Ownership: Strong leadership, technical communication, cross-functional collaboration, and a proactive driver mindset.
Qualification: BTech/BE/MTech/MS/MCA or equivalent.