Senior Consultant - AI Full Stack Developer

Invenio Business Solutions; Delhi; Hybrid; INR 1,200,000 - 2,400,000; Full time

Job summary

Invenio Business Solutions seeks an experienced architect and full-stack engineer to own the technical architecture of AI-enabled enterprise products from discovery to production. You will define boundaries, data models, and deployment topology while balancing performance, security, and cost.

You will lead architecture reviews, design RAG and AI agent workflows, and drive end-to-end implementations across backend services (Python), frontend dashboards, and SAP integrations leveraging SAP BTP and

Qualifications

• Experience delivering end-to-end production implementations.
• Bachelor’s or Master’s degree in CS/SE/IT/Data Science or related field.

Responsibilities

• 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.
• Lead PoCs and convert prototypes into production-grade implementations.
• Develop high-performance Python services and frontend dashboards.
• Design SAP integrations and SAP BTP extensions for enterprise environments.

Skills

Advanced Python

FastAPI

Django

React

TypeScript

REST/GraphQL

Microservices

Distributed systems

AI engineering

Leadership

Education

Bachelor's or Master’s degree in CS/Software Eng/IT/Data Science

Related discipline

Tools

Kafka

Redis

SQS/SNS

OpenAI LangChain

SAP BTP

SAP HANA Cloud

Docker

Kubernetes

Job description

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.

Bachelors 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.