Job Location: Hyderabad / Gurgaon

Work Mode: Hybrid

Qualification: B.E. / B.Tech / M.Tech

We are looking for a skilled Backend Developer with expertise in Generative AI to develop and implement AI-based applications for business use cases. The role involves working closely with key stakeholders to identify and define business problems, translate them into solution requirements, and deliver measurable business outcomes.

The candidate will work on complex, unstructured business challenges and leverage data-driven approaches to build impactful solutions.

Key Responsibilities

Agentic AI & LLM Orchestration

- Design, build, and maintain Agentic AI workflows using LangGraph / LangChain, including multi-agent coordination, tool invocation, memory, and state management.
- Develop deterministic and auditable agent flows suitable for enterprise-scale decisioning and analytics use cases.
- Implement prompt engineering strategies, guardrails, fallback mechanisms, and output validation to ensure reliable LLM responses.

Backend & Platform Engineering

- Architect and develop scalable Python-based backend services to support GenAI workloads.
- Build and expose APIs that integrate LLMs with structured and unstructured enterprise data sources.
- Ensure high availability, performance optimization, and fault tolerance across AI-driven backend systems.

NL-to-SQL & Data Intelligence

- Design and implement NL-to-SQL pipelines for structured datasets across multiple business domains.
- Apply schema grounding, semantic layers, query validation, and SQL safety mechanisms to improve accuracy and trust.
- Optimize generated queries for performance, explainability, and consistency across large datasets.

Observability, Governance & Quality

- Implement end-to-end observability for GenAI systems, including agent execution tracing, prompt/response logging, latency, and cost metrics.
- Define and monitor quality KPIs such as response accuracy, hallucination rates, and system reliability.
- Ensure compliance with enterprise security, privacy, and data governance standards.

Cloud, Deployment & Operations

- Design and deploy GenAI backend services on Azure using Azure OpenAI, AKS,



Functions, and App Services.
- Implement CI/CD pipelines and infrastructure-as-code for scalable and repeatable AI deployments.
- Partner with cloud and platform teams to optimize cost, scalability, and operational resilience.

Documentation & Reporting

- Document processes, pipelines, and architecture.
- Create clear and concise reports and dashboards to present findings and outcomes.

Required Skills
- Expert-level proficiency in Python for backend development, APIs, and asynchronous processing.
- Hands-on experience with LangGraph / LangChain for Agentic AI orchestration and workflow design.
- Strong understanding of Large Language Models (LLMs), prompt engineering, tool calling, and response validation.
- Experience designing and implementing Agentic AI systems with multi-agent coordination, memory, and state management.
- Proven expertise in NL-to-SQL architectures, including schema grounding, semantic layers, and SQL validation.
- Experience integrating LLMs with structured enterprise data sources and analytics platforms.
- Strong experience with Azure cloud services, including Azure OpenAI, AKS, Functions, and App Services.
- Experience building and deploying cloud-native microservices and RESTful APIs.
- Knowledge of observability for GenAI systems, including logging, tracing, latency, and cost monitoring.
- Experience implementing AI quality metrics such as accuracy, hallucination detection, and system reliability.
- Familiarity with CI/CD pipelines, DevOps practices, and infrastructure-as-code.
- Strong understanding of security, access control, and enterprise data governance.
- Experience building scalable, fault-tolerant, production-grade platforms beyond PoCs.




- Ability to drive architectural decisions and mentor senior and mid-level engineers.
- Proficiency with Git for source control, branching strategies, code reviews, and collaborative development.

Good to Have
- Experience with AWS, including Bedrock, Lambda, EKS, and S3.
- Exposure to multi-cloud or cloud-agnostic architectures for GenAI platforms.
- Hands-on experience with vector databases such as Pinecone, FAISS, Milvus, or Azure AI Search.
- Experience implementing RAG pipelines and hybrid search patterns.
- Familiarity with LLM evaluation frameworks and automated testing of AI outputs.
- Experience with model fine-tuning, embeddings optimization, or prompt versioning strategies.
- Knowledge of event-driven architectures and message brokers such as Kafka, Event Grid, and Service Bus.
- Experience with Docker and Kubernetes beyond basic deployment usage.
- Exposure to MLOps / LLMOps practices, including model lifecycle management and cost optimization.
- Experience working in regulated domains such as healthcare, life sciences, or financial services.
- Familiarity with data visualization or BI tools to support analytics-driven AI use cases.
- Contributions to open-source GenAI frameworks or internal AI accelerators.

Key Competencies Logical Thinking: Ability to think analytically and use a systematic and logical approach to analyze data, problems, and situations. Identify discrepancies and inconsistencies and collaborate effectively with analytical team members.

Task Management: Ability to plan tasks, prioritize work, support analytical projects, define data requirements and deliverables, and manage timelines effectively.

Communication: Ability to convey ideas and information clearly and accurately, both verbally and in writing, and collaborate effectively with team members and leadership.

Why Join Us?

- Play a key role in shaping the next generation of intelligent enterprise platforms.
- Work at the intersection of cutting-edge AI and real-world business impact.
- Collaborate with data scientists, engineers, and domain experts.
- Benefit from a versatile work environment focused on continuous learning and innovation.

📌 Back End Engineer (Gurugram)
🏢 CustomerInsights.AI
📍 Gurugram