Vacancy description
clarus advisers
India, Rāniganj
Integration Developer - SQL, C# .NET in Rāniganj, India is listed on Jobeax. Browse 30,000+ vacancies available.
A leading global professional services organization providing consulting, technology, audit, risk advisory, and tax services to clients across diverse industries. The organization combines deep industry expertise with advanced technologies such as AI, cloud, data analytics, cybersecurity, and digital transformation to help businesses solve complex challenges and drive sustainable growth. It operates across a broad international network and works with leading enterprises, governments, and institutions worldwide.
As an Applied AI Platform Engineer (BOOMI Integration Engineer), you will actively engage in your engineering craft, taking a hands-on approach to building the platforms, tooling, accelerators, and frameworks that other engineering teams build on. Your expertise will be pivotal in delivering platform capabilities that delight the engineers who depend on them, while driving tangible leverage and value across Deloitte's AI engineering investments. You will leverage your extensive engineering craftsmanship across platform engineering, distributed systems, and modern AI/ML and Data infrastructure, consistently demonstrating your strong track record in delivering high-quality, reusable, outcome-focused solutions. The ideal candidate will be a dependable team player, collaborating with cross-functional teams to design, build, and operate the enabling layer for AI engineering at scale.
Embrace and drive a culture of accountability for engineering-leverage and adoption outcomes. Build platform capabilities that solve recurring problems once, well, for many teams—reducing per-team build and operate toil while ensuring consistency and compliance by default through high-quality, lean designs and implementations.
Serve as the technical advocate for the platform as a product, ensuring capability integrity, feasibility, and alignment with the needs of the engineering teams who consume it. Lead requirement discovery with consuming teams, component design of platform services, frameworks, and the AI control plane, and their development, testing, integration, and support.
Maintain accountability for the integrity of the platform architecture and for the enterprise tech-stack conformance baseline that engineering teams build against. Manage platform dependencies, code design, implementation, the data and policy-as-code enforcement layers, and the OpenTelemetry-based instrumentation substrate—building capabilities that are operable, instrumented, performant, and drift-resistant by design, to the production standards set by SRE. Stay hands-on, self-driven, and continuously learn new approaches, languages, and frameworks. Create technical specifications, codify recurring patterns into reusable components and golden paths, and write high-quality, supportable, scalable code to ensure all platform KPIs (adoption, reliability-by-design, and developer experience) are met or exceeded. Develop lean platform capabilities through rapid, inexpensive experimentation to solve the real needs of consuming engineering teams. Adopt a mindset that favors action and evidence over extensive planning. Work collaboratively with empowered, cross-functional partners: engineering, SRE, security and risk, data governance, and engineering leadership and architecture. Possess expertise in platform engineering and modern AI/ML infrastructure—internal developer platforms, MLOps/LLMOps, model serving, retrieval and vector infrastructure, eval and observability tooling, policy-as-code, container orchestration, IaC, and CI/CD at platform scale—including AI and Agentic SSDLC to deliver self-service, governed capabilities with full automation from discovery to production to operations and all quality checks through the SSDLC lifecycle. Demonstrate strong understanding of the full lifecycle of platform and product development, focusing on continuous improvement and learning.
Quickly acquire domain knowledge of the enterprise data estate and the AI use-case patterns the platform must serve. Translate the needs of engineering teams, reference architectures, and governance requirements into reusable frameworks, the data platform, and governance-as-code enforcement. Be a valuable, flexible, and dedicated team member, supportive of teammates, and focused on quality and tech debt payoff.
Engage and collaborate with product engineering teams at all organizational levels, including customers as needed. Skills & Experience
A bachelor's degree in computer science, software engineering, data science, machine learning, or related discipline. Experience is the most relevant factor.
Hands On experience with BOOMI Integration is important.
5+ years of software and platform engineering experience with most of the following: Angular, React, NodeJS, Python(Mandatory), , C#, .NET, Java, Rust, SQL/NoSQL, PyTorch, TensorFlow, LangChain, LangGraph, LangSmith, LangFuse, Terraform, as well as unit, integration, and end-to-end testing frameworks & tools, specifically BDD, Gherkin, Cucumber, Playwright, and Selenium.
3+ years of experience designing, building, and operating AI/ML platform or infrastructure, with hands-on experience across building tooling for MLOps/LLMOps, model serving, retrieval and vector infrastructure, and eval/observability instrumentation for LLM integration (OpenAI, Anthropic, or open-source models).
3+ years of experience with cloud-native engineering on any of the cloud hyperscalers such as Azure, AWS, or GCP—including their AI/ML services such as Azure OpenAI, AWS Bedrock, or Vertex AI—as well as container orchestration (Kubernetes, Docker), Big Data, Databricks, CI/CD at platform scale, and distributed systems.
Prior experience with AI control-plane and agent-runtime patterns: model/LLM gateway, A2A and MCP integration, agent runtimes (e.g., Google ADK, Amazon Bedrock AgentCore), guardrails (PII redaction, prompt-injection, content, tool permissioning/tool-RBAC), policy-as-code, and multi-tenant isolation.
Prior experience with enterprise data platform engineering: data pipelines, self-service and data-product enablement, governance-as-code enforcement, and metadata/lineage.
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