Senior Consultant Fullstack Developer

Job Description

Role: Senior Consultant - Full Stack Developer Experience: 5 to 8 Years Location: India - Delhi Experience The candidate must have experience taking products from requirements and architecture through development, testing, deployment, production operations, enhancement, and stakeholder adoption with at least 3-4 complete, end-to-end production implementations. Bachelor’s or master’s degree in computer science, Software Engineering, Information Technology, Data Science, or a related discipline. Core responsibilities Architecture and technical leadership - Own the technical architecture of AI-enabled enterprise products from discovery to production. - Define application boundaries, service contracts, data models, integration patterns, deployment topology, and non-functional requirements. - Select appropriate technologies for LLM orchestration, data processing, vector search, workflow automation, and enterprise integration. - Produce architecture decision records, solution blueprints, sequence diagrams, API specifications, threat models, capacity plans, and operational runbooks. - Establish reusable reference architectures for RAG, AI agents, human-in-the-loop review, model gateways, and workflow automation. - Define standards for API design, schema validation, error handling, observability, testing, secure coding, versioning, and release management. - Review designs and code produced by other engineers. - Lead proof-of-concepts and convert successful prototypes into production-grade implementations. - Balance functional requirements, performance, reliability, security, cost, maintainability, and delivery timelines. Full-stack engineering - Develop high-performance Python services using FastAPI, Django, Flask, or equivalent frameworks. - Build asynchronous processing pipelines using Kafka, Redis Streams, AWS SQS/SNS, Azure Service Bus, or equivalent technologies. - Develop frontend applications using React, Next.js, JavaScript, HTML5, CSS3, and/or SAPUI5. - Build enterprise dashboards, AI workbenches, workflow consoles, document-review interfaces, search applications, approval journeys, and administrative tools. - Implement streaming AI responses, structured result views, confidence indicators, citations, source references, audit trails, and human feedback mechanisms. - Design and implement analytical, document, metadata, and vector data stores. - Build reusable shared libraries, SDKs, frontend components, API clients, authentication modules, and platform services. - Develop secure integrations with internal systems, third-party services, data platforms, and enterprise identity providers. AI and agentic systems - Design and productionize RAG systems using document ingestion, chunking, metadata enrichment, embeddings, reranking, retrieval filters, and response generation. - Implement AI agents and tool-using workflows with controlled planning, execution, validation, retries, approvals, and escalation. - Integrate LLMs from approved providers and model platforms, including enterprise model gateways and SAP Generative AI Hub where applicable. - Implement prompt versioning, model versioning, evaluation datasets, golden test cases, regression suites, and quality gates. - Establish controls for hallucination, prompt injection, data leakage, unsafe tool use, unauthorized access, and excessive model autonomy. - Design model fallback, timeout, retry, caching, token-budget, and cost-control strategies. - Implement observability for prompts, responses, model metadata, tool calls, latency, token usage, retrieval quality, user feedback, and failure modes. - Collaborate with data scientists and platform engineers on model training, fine-tuning, inference optimization, deployment, and monitoring. - Build AI features that support structured extraction, classification, summarization, recommendations, forecasting, conversational search, and business-process automation. SAP BTP and enterprise SAP responsibilities - Lead the design and implementation of SAP BTP extension and side-by-side applications. - Design integrations between SAP BTP, SAP S/4HANA, SAP ECC, SAP SuccessFactors, SAP Ariba, SAP Analytics Cloud, and non-SAP systems. - Use OData, REST, RFC, events, SAP Gateway, and SAP Integration Suite as appropriate. - Work with SAP BTP services such as: - SAP HANA Cloud, SAP AI Core, SAP AI Launchpad, SAP Generative AI Hub, SAP Integration Suite. - SAP Destination and Connectivity services, SAP Business Application Studio, SAP Cloud Foundry. - Design SAP-aware RAG solutions using business-process context, authorization-aware retrieval, SAP metadata, and HANA Cloud vector capabilities. - Integrate SAP Joule to build AI assistants and agents that interact with SAP business processes under controlled authorization. Required technical skills Backend and platform engineering - Advanced Python and strong software design ability. - FastAPI, Django, Flask, Pydantic, SQLAlchemy, and asynchronous programming. - TypeScript/Node.js for CAP services and enterprise integrations. - REST, GraphQL where appropriate, OpenAPI, WebSockets, event-driven systems, and messaging. - PostgreSQL, SAP HANA Cloud, MongoDB, Redis, Elasticsearch/OpenSearch, and vector databases. - Microservices, modular monoliths, distributed systems, caching, idempotency, retries, circuit breakers, and resiliency patterns. Frontend engineering - Advanced JavaScript, React, Next.js, component architecture, state management, frontend security, and testing. - SAPUI5 and Fiori development or the ability to lead SAP UI implementation. - Accessibility, responsive design, performance optimization, design systems, and user-centered AI experiences. - Experience presenting complex AI outputs, evidence, citations, confidence values, structured data, and workflow states. AI engineering - LLM application architecture and production integration. - RAG, embeddings, reranking, vector databases, semantic search, hybrid search, and document processing. - Agentic workflows, function calling, tool execution, workflow graphs, and human approval patterns. - Prompt engineering, structured generation, JSON schema validation, guardrails, and model routing. - Model evaluation, benchmark creation, regression testing, red teaming, safety testing, and feedback analysis. - Fine-tuning or parameter-efficient adaptation of open-source models is preferred. - Experience with LangChain, LlamaIndex, Haystack, Semantic Kernel, DSPy, or comparable frameworks. - Understanding of GPU inference, model serving, batching, quantization, latency, throughput, and cost optimization is advantageous. SAP and integration - Strong experience in SAP BTP application deployment using CDS, SAP HANA Cloud, Cloud Foundry, and/or Kyma. - SAP AI Core, AI Launchpad, Generative AI Hub, Document AI, and SAP Business AI. - SAP S/4HANA integration using OData, REST, RFC, events, and Integration Suite. - SAPUI5/Fiori, SAP Gateway, OAuth 2.0, OpenID Connect, SAML, XSUAA, role collections, technical users, and enterprise. - Understanding of SAP clean-core, side-by-side extensibility, tenant isolation, and lifecycle management. - SAP BTP or SAP AI certification is preferred. Cloud, DevOps, and operations - AWS, Azure, Google Cloud, and/or SAP BTP. - Docker, Kubernetes, Cloud Foundry, and infrastructure-as-code. - CI/CD with GitHub Actions, GitLab, Azure DevOps, or equivalent. - OpenTelemetry, logging, metrics, distributed tracing, dashboards, and alerting. - Secrets management, vulnerability scanning, dependency management, image scanning, and software supply-chain security. - Disaster recovery, backup, rollback, blue-green or canary release strategies, capacity planning, and production incident management. Leadership and delivery responsibilities - Mentor developers and establish engineering standards. - Lead technical discussions with product managers, business analysts, architects, security teams, SAP teams, and senior stakeholders. - Translate business requirements into technical designs, user stories, acceptance criteria, and implementation plans. - Establish requirements traceability from BRD or process requirements to architecture, code, test cases, deployment, and production evidence. - Lead estimation, sprint planning, backlog refinement, technical risk management, and delivery reviews. - Conduct design, code, threat-model, and production-readiness reviews. - Coordinate cross-functional delivery across backend, frontend, AI, data, DevOps, SAP, QA, and support teams. - Define coding standards, branching strategies, release policies, quality gates, and engineering metrics. - Create technical training, reusable templates, reference implementations, and internal documentation. - Participate in on-call escalation, incident analysis, root-cause analysis, and post-incident improvement.