Job Description

Locations

• India - Hyderabad

Skills Required

• Computer Scienceintermediate
• software developmentintermediate
• Pythonintermediate
• promptintermediate
• automated testingintermediate
• modern web applications using TypeScriptintermediate
• SQLintermediate
• OpenAI APIsintermediate
• Amazon Bedrockintermediate
• Databricksintermediate
• Power Automateintermediate
• modelintermediate

Required Qualifications

• Any degree and 5 to 9 years of relevant experience in Computer Science, Information Technology, Engineering, Data Science, or a related field (experience, 9 years)
• Any degree and 5 to 9 years of relevant experience in Computer Science, Information Technology, Engineering, Data Science, or a related field (experience, 9 years)
• Must-Have Skills (experience)
• 4 or more years of professional experience in software development, application engineering, systems integration, or system design. (experience)
• 2 or more years of hands-on experience building, deploying, or supporting production GenAI applications, assistants, agents, Custom GPTs, RAG solutions, or comparable AI-enabled software. (experience)
• Strong hands-on proficiency in Python and experience developing, testing, debugging, and maintaining production applications, APIs, data services, or integration components. (experience)
• Hands-on experience with prompt and context design, embeddings, vector databases or search, retrieval pipelines, structured outputs, tool integration, agent orchestration, evaluation, guardrails, and observability. (experience)
• Experience designing and building cloud-native solutions, preferably on AWS, using services such as Amazon Bedrock, AWS Lambda, Amazon EKS or ECS, API Gateway, IAM, networking, monitoring, and event-driven integration. (experience)
• Experience designing secure RESTful or GraphQL integrations using API gateways, OAuth, service identities, enterprise authentication, role-based access, secrets management, and least-privilege principles. (experience)
• Experience with automated testing, source control, code review, Git-based workflows, CI/CD, and responsible use of AI-assisted software development tools. (experience)
• Experience building modern web applications using TypeScript, React, Next.js, component-driven architecture, state management, design systems, and API integration. (experience)
• Working knowledge of SQL, files, databases, and governed data-access patterns, with strong troubleshooting and analytical skills across application, model, data, integration, and infrastructure layers. (experience)
• 4 or more years of professional experience in software development, application engineering, systems integration, or system design. (experience)
• 2 or more years of hands-on experience building, deploying, or supporting production GenAI applications, assistants, agents, Custom GPTs, RAG solutions, or comparable AI-enabled software. (experience)
• Strong hands-on proficiency in Python and experience developing, testing, debugging, and maintaining production applications, APIs, data services, or integration components. (experience)
• Hands-on experience with prompt and context design, embeddings, vector databases or search, retrieval pipelines, structured outputs, tool integration, agent orchestration, evaluation, guardrails, and observability. (experience)
• Experience designing and building cloud-native solutions, preferably on AWS, using services such as Amazon Bedrock, AWS Lambda, Amazon EKS or ECS, API Gateway, IAM, networking, monitoring, and event-driven integration. (experience)
• Experience designing secure RESTful or GraphQL integrations using API gateways, OAuth, service identities, enterprise authentication, role-based access, secrets management, and least-privilege principles. (experience)
• Experience with automated testing, source control, code review, Git-based workflows, CI/CD, and responsible use of AI-assisted software development tools. (experience)
• Experience building modern web applications using TypeScript, React, Next.js, component-driven architecture, state management, design systems, and API integration. (experience)
• Working knowledge of SQL, files, databases, and governed data-access patterns, with strong troubleshooting and analytical skills across application, model, data, integration, and infrastructure layers. (experience)
• Professional Skills (experience)
• Strong verbal and written communication skills and the ability to produce clear technical documentation. (experience)
• Ability to collaborate effectively with global, distributed, cross-functional teams using agile or scaled agile ways of working. (experience)
• High degree of initiative, adaptability, ownership, and self-motivation while delivering high-quality outcomes with appropriate guidance. (degree)
• Ability to manage multiple priorities, assess gaps and fit, communicate technical risks and tradeoffs, and support shared team outcomes. (experience)
• Strong verbal and written communication skills and the ability to produce clear technical documentation. (experience)
• Ability to collaborate effectively with global, distributed, cross-functional teams using agile or scaled agile ways of working. (experience)
• High degree of initiative, adaptability, ownership, and self-motivation while delivering high-quality outcomes with appropriate guidance. (degree)
• Ability to manage multiple priorities, assess gaps and fit, communicate technical risks and tradeoffs, and support shared team outcomes. (experience)

Preferred Qualifications

• Experience with OpenAI APIs and SDKs, MCP servers and Apps, ChatGPT Skills, plugins, Workspace Agents, LangChain, LangGraph, n8n, or comparable agent technologies. (experience)
• Experience with Amazon Bedrock and related agent capabilities, AWS Lambda, Amazon EKS or ECS, Amazon EC2, API Gateway, IAM, VPC networking, or event-driven services. (experience)
• Experience with Databricks, Unity Catalog, Vector Search, SQL Warehouses, semantic layers, or governed natural-language-to-SQL solutions. (experience)
• Experience with Power Automate, UiPath, Tableau, Jira, Smartsheet, or comparable automation and delivery tools. (experience)
• Experience with model and application evaluation, red teaming, responsible AI, AI security, GxP, computer-system validation, regulated technology environments, or relevant technical certifications. (experience)
• Experience with OpenAI APIs and SDKs, MCP servers and Apps, ChatGPT Skills, plugins, Workspace Agents, LangChain, LangGraph, n8n, or comparable agent technologies. (experience)
• Experience with Amazon Bedrock and related agent capabilities, AWS Lambda, Amazon EKS or ECS, Amazon EC2, API Gateway, IAM, VPC networking, or event-driven services. (experience)
• Experience with Databricks, Unity Catalog, Vector Search, SQL Warehouses, semantic layers, or governed natural-language-to-SQL solutions. (experience)
• Experience with Power Automate, UiPath, Tableau, Jira, Smartsheet, or comparable automation and delivery tools. (experience)
• Experience with model and application evaluation, red teaming, responsible AI, AI security, GxP, computer-system validation, regulated technology environments, or relevant technical certifications. (experience)

Responsibilities

• GenAI Solution Delivery
• Design, develop, test, and deploy LLM-powered assistants, agents, Custom GPTs, RAG solutions, APIs, and AI-enabled applications on ChatGPT Enterprise and other approved platforms.
• Implement prompt and context strategies, structured outputs, embeddings, vector or hybrid retrieval, tool calling, workflow orchestration, and human-in-the-loop controls.
• Build scalable RAG pipelines that ground responses in trusted enterprise data.
• Contribute to the GenAI capability roadmap through implementation of MCP-based Apps, ChatGPT Skills, plugins, Workspace Agents, and reusable integration patterns.
• Develop integration layers and event-driven workflows using APIs, RPA, n8n, and approved orchestration tools to connect systems and trigger agent workflows.
• Develop reusable Python services, libraries, templates, and evaluation utilities, and contribute TypeScript, React, or Next.js components when required by the solution.
• Design, develop, test, and deploy LLM-powered assistants, agents, Custom GPTs, RAG solutions, APIs, and AI-enabled applications on ChatGPT Enterprise and other approved platforms.
• Implement prompt and context strategies, structured outputs, embeddings, vector or hybrid retrieval, tool calling, workflow orchestration, and human-in-the-loop controls.
• Build scalable RAG pipelines that ground responses in trusted enterprise data.
• Contribute to the GenAI capability roadmap through implementation of MCP-based Apps, ChatGPT Skills, plugins, Workspace Agents, and reusable integration patterns.
• Develop integration layers and event-driven workflows using APIs, RPA, n8n, and approved orchestration tools to connect systems and trigger agent workflows.
• Develop reusable Python services, libraries, templates, and evaluation utilities, and contribute TypeScript, React, or Next.js components when required by the solution.
• Quality, Security, and Operations
• Implement automated unit, integration evaluation, and end-to-end tests, along with tracing and monitoring for retrieval quality, response consistency and reliability.
• Implement safeguards for prompt injection, data leakage, unauthorized retrieval, insecure tool execution, secrets exposure, excessive agency, and inappropriate model outputs.
• Apply secure integration and identity patterns using API gateways, OAuth, service identities, delegated authorization, role-based access control, secrets management, least privilege, and user-level auditability.
• Build and maintain CI/CD pipelines and approved cloud deployment configurations using AWS services, containers, serverless compute, or managed platform capabilities.
• Troubleshoot defects across application, model, retrieval, data, integration, and infrastructure layers; support incident investigation, remediation, rollback, patching, and performance tuning.
• Create and maintain technical designs, test evidence, runbooks, deployment instructions, and handover materials that support sustainable operations.
• Implement automated unit, integration evaluation, and end-to-end tests, along with tracing and monitoring for retrieval quality, response consistency and reliability.
• Implement safeguards for prompt injection, data leakage, unauthorized retrieval, insecure tool execution, secrets exposure, excessive agency, and inappropriate model outputs.
• Apply secure integration and identity patterns using API gateways, OAuth, service identities, delegated authorization, role-based access control, secrets management, least privilege, and user-level auditability.
• Build and maintain CI/CD pipelines and approved cloud deployment configurations using AWS services, containers, serverless compute, or managed platform capabilities.
• Troubleshoot defects across application, model, retrieval, data, integration, and infrastructure layers; support incident investigation, remediation, rollback, patching, and performance tuning.
• Create and maintain technical designs, test evidence, runbooks, deployment instructions, and handover materials that support sustainable operations.
• Collaboration and Enablement
• Partner with functional teams, product owners, architects, business SMEs, data engineers, platform teams, DevOps, cybersecurity, privacy, and responsible AI stakeholders to translate requirements into reliable solutions.
• Participate in agile planning, backlog refinement, estimation, demonstrations, retrospectives, release activities, and technical design reviews.
• Review code and technical designs, apply software engineering standards, and use AI-assisted development tools such as OpenAI Codex or GitHub Copilot with appropriate review, security, and quality controls.
• Support office hours, technical enablement sessions, junior engineers, and knowledge-sharing through reusable examples, engineering patterns, and lessons learned.
• Communicate delivery status, technical risks, dependencies, defects, and recommendations clearly to technical and non-technical stakeholders.
• Partner with functional teams, product owners, architects, business SMEs, data engineers, platform teams, DevOps, cybersecurity, privacy, and responsible AI stakeholders to translate requirements into reliable solutions.
• Participate in agile planning, backlog refinement, estimation, demonstrations, retrospectives, release activities, and technical design reviews.
• Review code and technical designs, apply software engineering standards, and use AI-assisted development tools such as OpenAI Codex or GitHub Copilot with appropriate review, security, and quality controls.
• Support office hours, technical enablement sessions, junior engineers, and knowledge-sharing through reusable examples, engineering patterns, and lessons learned.
• Communicate delivery status, technical risks, dependencies, defects, and recommendations clearly to technical and non-technical stakeholders.

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