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.