Senior Platform Engineer

Sutra.AI

Greater Delhi Area

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 fast, 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 new 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.

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