New opportunity

Senior / Lead Forward Deployed Engineer (FDE) - Cogentiq Platform

Fractal · Bengaluru, India

About this role

Fractal is looking for a Senior / Lead Forward Deployed Engineer (FDE) to join the founding Cogentiq team in Bengaluru. In this role, you will work at the intersection of software engineering, AI, enterprise integration, and customer success to deploy intelligent AI agents and enterprise AI solutions into complex environments.

Responsibilities

Design and develop production-grade AI applications using the Cogentiq platform.
Deploy enterprise AI solutions from proof of concept to production.
Build integrations with enterprise applications such as SAP, Salesforce, Workday, ServiceNow, SharePoint, Snowflake, Databricks, and other business systems.
Develop reusable connectors, APIs, SDKs, MCP servers, and automation components.
Design secure, scalable, cloud-native architectures and build reliable backend services and microservices.
Collaborate directly with customer engineering and business teams to understand requirements, conduct technical workshops, and deliver successful AI solutions.
Troubleshoot production deployments, optimize system performance, and ensure reliability and security.
Capture learnings from customer deployments and contribute reusable capabilities back to the Cogentiq platform.
Work closely with AI engineers, platform engineers, product managers, and solution architects to continuously improve the platform.

Required qualifications

Strong software engineering fundamentals with proficiency in Python (experience in Java or TypeScript is a plus).
Experience building production-grade backend applications, REST APIs, microservices, distributed systems, databases, Git, and modern software development practices.
Hands-on experience building AI-powered applications using Large Language Models (LLMs), Agentic AI frameworks, Retrieval-Augmented Generation (RAG), tool calling, prompt engineering, and AI evaluation techniques.
Experience integrating enterprise applications and platforms such as SAP, Salesforce, Workday, ServiceNow, SharePoint, Snowflake, Databricks, and other business systems.
Proficiency in enterprise integration patterns, API-first architectures, authentication mechanisms (OAuth, JWT, SAML), SQL, REST, and GraphQL APIs.
Experience building and deploying cloud-native applications on Azure, AWS, or Google Cloud using Docker and Kubernetes, along with CI/CD pipelines and event-driven architectures.
Strong customer and product mindset with the ability to communicate effectively with technical and business stakeholders.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

PythonJavaTypeScriptLarge Language Models (LLMs)Agentic AIRetrieval-Augmented Generation (RAG)Prompt EngineeringModel Context Protocol (MCP)SAPSalesforceWorkdayServiceNowSharePointSnowflakeDatabricksSQLREST APIsGraphQLAzureAWSGoogle CloudDockerKubernetes

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Enterprise AI Solution Deployment
Design, develop, and deploy production-grade AI applications and multi-agent workflows using the Cogentiq platform, taking solutions smoothly from proof of concept to production environments.
Complex Enterprise Integration
Build robust, secure integrations with core business systems such as SAP, Salesforce, Workday, ServiceNow, SharePoint, Snowflake, and Databricks using REST, GraphQL, SQL, and appropriate authentication mechanisms.
Platform Evolution & Reusability
Capture insights and learnings from direct customer deployments to develop reusable connectors, APIs, SDKs, MCP servers, and platform capabilities back into the core Cogentiq ecosystem.
Customer Collaboration & Technical Workshops
Partner directly with enterprise customer engineering and business teams to understand operational challenges, lead architecture discussions, and conduct technical workshops that drive successful adoption.

Likely challenges to think through

Balancing Custom Client Implementations with Core Platform Scalability
FDEs risk getting pulled into highly customized, one-off client work that diverges from core product architecture. To prepare, practice identifying generic patterns during customer engagements and abstracting them into reusable platform components, connectors, or SDKs.
Integrating Disparate Enterprise Systems with Emerging Agentic AI
Connecting legacy enterprise platforms like SAP or Workday with modern LLM frameworks, RAG pipelines, and Model Context Protocol (MCP) servers introduces security, latency, and authentication friction. Prepare by reviewing secure enterprise integration patterns (OAuth, JWT, SAML) alongside modern AI orchestration frameworks.
Managing Ambiguity in Rapidly Evolving Greenfield Environments
As part of the founding team for a flagship enterprise platform, requirements and customer use cases may shift quickly. Prepare by demonstrating an ability to prototype rapidly under uncertainty, manage stakeholder expectations, and translate ambiguous business needs into concrete technical specifications.

Practice your answers

Technical practice

Designing Production-Grade AI Agent Architectures

What to coverCandidates should discuss how they design scalable, robust multi-agent workflows using LLM frameworks, handling agent communication, state management, and error recovery. Emphasize mechanisms for robust observability, evaluation, and guardrails across cloud environments.

Enterprise Application Integration Patterns

What to coverExplain how you design secure, reliable integrations with enterprise systems like SAP, Salesforce, or Workday using REST, GraphQL, or SQL. Address handling authentication mechanisms such as OAuth, JWT, and SAML while maintaining API-first architectures.

Implementing Retrieval-Augmented Generation (RAG) and Tool Calling

What to coverDiscuss strategies for building accurate RAG pipelines and robust tool-calling mechanisms for LLMs grounded in enterprise context. Highlight how you handle context windows, latency optimization, and semantic search integration.

Model Context Protocol (MCP) and Reusable Connectors

What to coverDescribe the principles of the Model Context Protocol (MCP) and how you develop reusable connectors, SDKs, or automation components. Explain how abstraction allows customer-specific implementations to be turned back into reusable platform capabilities.

Cloud-Native Microservices Deployment with Kubernetes

What to coverExplain your approach to containerizing Python backend services with Docker and deploying them on Kubernetes across cloud platforms like AWS, Azure, or GCP. Cover CI/CD pipelines, event-driven architectures, and messaging platform integration.

Troubleshooting and Optimizing AI Production Deployments

What to coverDetail your methodology for monitoring, debugging, and optimizing production-grade AI applications and microservices under heavy load. Discuss how you ensure system reliability, security, and performance tuning for low-latency responses.

Authentication and Security Best Practices in Distributed Systems

What to coverAddress how you secure distributed backend services and enterprise integrations against common vulnerabilities. Discuss token management, role-based access control (RBAC), and encryption standards in cloud-native environments.

AI Evaluation Techniques and Guardrails

What to coverExplain how you evaluate the performance, safety, and reliability of LLM-based applications before and after production deployment. Discuss evaluation frameworks, guardrails implementation, and monitoring drift in enterprise contexts.

Behavioural practice

Translating Ambiguous Customer Challenges into Technical Solutions

What to coverDescribe a situation where you worked directly with enterprise stakeholders to understand an ambiguous business problem. Outline the situation, the technical approach you designed and prototyped, and the resulting impact on the client's workflow.

Balancing Custom Client Implementations with Reusable Platform Architecture

What to coverShare an example where you built a custom solution for a customer while simultaneously identifying patterns to make it reusable. Explain how you balanced client delivery pressure with long-term platform scalability and component reusability.

Leading Technical Workshops and Cross-Functional Collaboration

What to coverRecount a time when you led technical workshops or collaborated closely with product managers, solution architects, and customer engineering teams. Detail how you aligned technical and business stakeholders to drive a complex deployment forward.

Troubleshooting a Critical Production Failure Under Pressure

What to coverProvide an instance where you had to troubleshoot and resolve a severe issue in a production deployment under tight deadlines. Explain your diagnostic steps, how you coordinated with team members, and the measures you took to prevent recurrence.

Sharpen your knowledge

Topics to prepare

Enterprise Integration Patterns and Architecture
Review API-first architectures, event-driven design, and secure enterprise authentication mechanisms including OAuth, JWT, and SAML. Exercise: Design a secure integration blueprint connecting a cloud platform to systems like Salesforce or SAP with token-based authentication and rate limiting. Relevance: Directly required for building production-grade integrations with complex enterprise business systems.
Agentic AI Frameworks and RAG Pipelines
Study multi-agent workflows, tool calling, retrieval-augmented generation architectures, and evaluation techniques for LLMs. Exercise: Build a multi-step agentic prototype in Python that dynamically invokes external tools and evaluates output accuracy against enterprise context. Relevance: Essential for developing production-grade AI applications using the Cogentiq platform.
Cloud-Native Architecture and Containerization
Examine microservices design, container deployment using Docker and Kubernetes, and CI/CD pipeline best practices. Exercise: Containerize a Python backend microservice and write Kubernetes deployment manifests with health checks and auto-scaling rules. Relevance: Required for deploying scalable, reliable enterprise AI solutions across cloud environments like AWS, Azure, or GCP.

Skills to revise

Python Backend and API Development
Practice writing robust Python code for backend services, REST APIs, and microservices interacting with SQL databases. Exercise: Implement a production-grade REST API endpoint with structured logging, error handling, and database integration using Python. Relevance: Core programming requirement for developing platform components and custom connectors.
Model Context Protocol (MCP) and Orchestration
Gain hands-on familiarity with the Model Context Protocol and modern AI orchestration frameworks. Exercise: Implement a custom MCP server wrapper in Python to expose internal tools and data sources to an LLM agent framework. Relevance: Explicitly highlighted as a highly desirable skill for building reusable connectors and automation components within Cogentiq.
Cloud Deployment and Container Orchestration
Gain practical experience with Docker, Kubernetes, and cloud platforms (Azure, AWS, or GCP). Exercise: Deploy a containerized microservice onto a local Minikube cluster and configure monitoring/observability hooks. Relevance: Directly tied to deploying and troubleshooting production-grade enterprise workloads.

Your preparation checklist

Build a Python-Based Agentic AI Prototype
Develop a functional proof-of-concept application using Large Language Models (LLMs), RAG, and tool calling in Python, ensuring it integrates with external APIs or mock enterprise data sources.
Design Enterprise Integration Architecture
Draft a system architecture diagram and technical write-up demonstrating how to securely integrate cloud-native services with enterprise platforms like SAP or Salesforce using OAuth, JWT, or SAML authentication.
Prepare Cloud and Containerization Artifacts
Containerize a sample backend service using Docker and write Kubernetes deployment configuration files complete with health checks and CI/CD pipeline integration notes.
Create a Case Study on Reusable Connectors and SDKs
Prepare a structured project walkthrough showing how custom customer implementations were abstracted into reusable platform capabilities, APIs, SDKs, or MCP servers.
Formulate Technical and Customer-Facing Questions
Draft thoughtful questions for the interviewers regarding the Cogentiq platform architecture, governance frameworks, enterprise onboarding workflows, and cross-functional collaboration between engineering and customer success.

Availability last checked: 4 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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