About CustomerInsights.AI
CustomerInsights.AI is a global analytics and AI-driven company founded in 2018, enabling data-driven commercial decision-making for Life Sciences organizations. Our product ecosystem, including ciPARTHENON and ciATHENA, leverages Analytics Automation, Artificial Intelligence, and Machine Learning to deliver timely, actionable insights to key stakeholders. With teams across North America and India, we work with client organizations ranging from emerging startups to large enterprises.
Position Overview:
We are seeking a skilled professional with expertise in Generative AI to develop and implement AI-based applications for business use cases. This role involves close collaboration with key stakeholders to identify and define business problems, translate them into solution requirements, and drive effective outcomes. The individual will be responsible for delivering measurable business value, communicating insights, and presenting results to stakeholders. The role requires the ability to 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. o Optimize generated queries for performance, explainability, and consistency across large datasets.
- Observability, Governance & Quality o 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 (Azure): 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. o Partner with cloud and platform teams to optimize cost, scalability, and operational resilience.
- Documentation and 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.
Logical Thinking – Able to think analytically, use a systematic and logical approach to analyze data, problems, and situations. Spot discrepancies and inconsistencies in information and materials. Collaborate effectively with other analytical team members
Task Management – Basic level of project management knowledge and experience. Should be able to plan tasks, discuss and work on priorities. Support analytical projects by formulating methodology, establish data requirements, identifying client deliverables, determine tasks and timing. Demonstrate initiative to improve quality and customer service by striving to exceed customer expectations. Balance team and individual responsibilities and put the success of the team above own interests.
Communication – Convey ideas and information clearly and accurately to self or others in writing and verbally. Establish effective mechanisms of communication with team, and the organization’s leadership to foster an environment of openness, trust and teamwork.
Good to have:
- Experience with AWS (e.g., Bedrock, Lambda, EKS, S3) in addition to Azure-based deployments
- Exposure to multi-cloud or cloud-agnostic architectures for GenAI platforms
- Hands-on experience with vector databases (e.g., Pinecone, FAISS, Milvus, Azure AI Search)
- Experience implementing RAG (Retrieval-Augmented Generation) 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 (Kafka, Event Grid, 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
Qualification: B. E. / B. Tech / M. Tech
Job Location: Hyderabad, Gurgaon
Work Mode: Hybrid
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 passionate data scientists, engineers and domain experts
- Benefit from a flexible work environment focused on continuous learning and innovation
Culture & Values
We foster a high-ownership, performance-driven culture where teams are encouraged to think creatively, act responsibly, and deliver measurable outcomes. Our values guide how we collaborate, make decisions, and build long-term partnerships.
Email us at: [email protected]
Website: https://www.customerinsights.ai/
LinkedIn: https://www.linkedin.com/company/customer-insights-ai/