Senior Platform Engineer

Type: Full-time

Location: Remote (Candidates should be based out of Delhi NCR / Bhopal)

Experience: 6-10 years total · 3+ years owning platform architecture or data infrastructure in production

About Sutra.AI

Sutra.AI is a complete 'Data to Value' AI business transformation platform designed specifically for Business Builders. Unlike other platforms that cater to AI Builders or large enterprises with substantial budgets, Sutra.AI is purpose-built for mid-market businesses without the need of AI expertise. Developed over 4 years, Sutra.AI is a patent-pending, fully orchestrated AI SaaS platform.

It identifies and executes AI use cases to enhance operational efficiency, customer experience, revenue growth, profitability, innovation, and risk reduction.

We are in scale-up mode - shipping quick, onboarding customers continuously, and standardizing the platform engineering patterns that compound across every customer and product line.

About the Role

This is a hands-on engineering role with architectural ownership. You will spend the majority of your time designing and writing code, not running a program office and not managing a team. You will take ambiguous requirements from customer environments - undocumented schemas, legacy ERP systems, inconsistent data quality and turn them into reliable, repeatable platform capability.

You will operate with a high degree of independence, make pragmatic build-versus-buy calls, and be accountable for how the platform performs, scales, and stays up in front of customers.

What You'll Do

1. Platform Architecture & System Design

- Own the architecture of Sutra.AI’s core platform services - service boundaries, API contracts, data models, and integration patterns across the stack.
- Translate solution and customer requirements into concrete engineering approaches, reference architectures, and build sequences the wider team can execute against.
- Design for multi-tenancy, scale, and reuse so that platform capability built for one customer generalizes to the next.
- Proactively challenge architectural gaps, evaluate technical feasibility, and make pragmatic build-versus-configure decisions with clear trade-off reasoning.
- Drive technical decisions through design documents, architecture reviews, and working prototypes rather than opinion.

2. Data Infrastructure & Pipelines

- Design, build, and operate ingestion pipelines from diverse customer systems - ERP, CRM, MES, relational and non-relational databases, files, and APIs.
- Own batch and streaming data processing, orchestration, transformation layers, and the storage architecture underneath analytics and AI workloads.
- Build data quality, schema-drift detection, lineage, and reconciliation controls so downstream models and dashboards can be trusted.
- Model relationships between operational systems, workflows, and data sources where documentation does not exist, and formalize what you discover.
- Optimize query performance, storage cost,



and pipeline throughput as data volumes and customer count grow.
- Build and maintain the data foundations that AI workloads depend on, including retrieval, embedding, and feature pipelines.

3. Security, Governance & Multi-Tenancy

- Implement tenant isolation, access control, secrets management, and encryption standards across platform and data layers.
- Build the governance controls - audit trails, retention, PII handling - required to operate credibly in enterprise and regulated customer environments.
- Partner with engineering leadership to align platform practices to compliance frameworks relevant to our customers.

4. Engineering Standards & Technical Leadership

- Establish platform engineering standards, reusable components, internal libraries, and documentation that raise the whole team’s throughput.
- Mentor engineers through design discussions, code review, and pair programming; lead by example on code quality.
- Identify engineering and operational risks early, design mitigation plans, and resolve them with stakeholders rather than only reporting them.
- Collaborate with Implementation, Solutioning, and Product teams so platform decisions stay tied to customer value and delivery timelines.

Requirements Must-Have

- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical discipline.
- 6-10 years of software engineering experience, with at least 3 years owning platform, infrastructure, or data engineering systems in production.
- Demonstrated architecture ownership - you have designed systems end to end, not only implemented someone else’s design.
- Strong backend engineering skills in Python (and comfort with Node.js/TypeScript services), including APIs, async systems, and distributed architectures.
- Deep data engineering experience: ETL/ELT design, batch and stream processing, orchestration, and warehouse or lakehouse modelling.
- Strong SQL and database engineering - schema design, query optimization, indexing, and performance tuning across relational and NoSQL stores.
- Hands-on cloud experience (AWS primary; Azure or GCP acceptable) including compute, storage, managed databases, networking, and IAM.
- Working experience with containerization, CI/CD, and infrastructure-as-code.
- Proven end-to-end ownership: systems you shipped independently from problem statement to production and then operated.
- Strong analytical and debugging skills across layers - data quality, application behaviour, infrastructure, and system design.
- Ability to work through ambiguity: undocumented systems, incomplete requirements, and competing priorities across concurrent engagements.




- Clear written and verbal communication, including the ability to explain technical trade-offs to non-technical stakeholders.

Preferred

- Experience in startups or fast-growth product environments with high ownership and low process overhead.
- Experience building platform or data foundations for AI/ML workloads - retrieval systems, vector stores, feature or embedding pipelines.
- Multi-tenant B2B SaaS platform experience.
- Exposure to enterprise system integration (ERP, CRM) and to industrial domains such as manufacturing, supply chain, or production.
- Familiarity with compliance frameworks (ISO 27001, SOC 2) and enterprise security review processes.
- Open-source contributions, published technical writing, or internal platform tooling you built and others adopted.

Core Technical Competencies Languages: Python (primary), SQL, TypeScript/JavaScript; Bash for automation

Data Engineering: Airflow or equivalent orchestration, dbt, Spark, Kafka or similar streaming, batch and incremental pipeline design

Storage & Query: PostgreSQL, SQL Server, MySQL, MongoDB, object storage, columnar warehouses, Trino or equivalent query engines

Cloud & Infrastructure: AWS (EC2, S3, RDS, Lambda, IAM, VPC) primary; Azure/GCP secondary; Docker, Kubernetes, Terraform

DevOps & Reliability: CI/CD pipelines, Git workflows, monitoring and alerting, logging and tracing, incident response

APIs & Services: REST and GraphQL design, FastAPI, Node.js/Express, event-driven and microservices patterns, authentication and authorization (OAuth, JWT)

AI Platform Exposure: Vector databases (Pinecone, Weaviate, Qdrant, FAISS), RAG and retrieval infrastructure, LLM orchestration frameworks

Security & Governance: Tenant isolation, secrets management, encryption at rest and in transit, access control, audit logging

Success Metrics

- Platform Reliability: uptime, performance, and stability of platform services and pipelines in customer environments.
- Data Trust: pipeline success rates, data quality issues caught before delivery, and reduction in analytics and AI defects traced to data.
- Delivery Leverage: reduction in time and effort to onboard a recent customer or stand up a new use case on existing platform capability.
- Engineering Quality: first-time build quality, defect rates, and reduction in quality-driven escalations across engagements.
- Efficiency: cloud and infrastructure cost per customer as scale increases.

The Ideal Candidate

- Builder-owner: you take a problem from an ambiguous statement to running a production system without needing constant direction.
- Systems thinker: you see how components interact, and you design for the failure modes as much as the happy path.
- Pragmatic: you optimize for what the business needs now while keeping the door open for what it will need next, and you can tell those apart.
- Detective mindset with data: undocumented schemas and messy source systems are a puzzle to solve, not a blocker to escalate.

📌 Senior Platform Engineer (Delhi)
🏢 Sutra.AI
📍 Delhi