Job Title: Senior AI Applications Engineer
Work Location: Vadodara, Gujarat / Mohali, Punjab
Work Arrangement: This position is onsite. Candidates must be available for client meetings, technical working sessions, deployments, and occasional production-support activities.
Senior AI Applications Engineer to build, integrate, deploy, and improve secure, production-ready AI and automation applications for client and internal use.
This is a senior hands-on engineering role. The successful candidate will design detailed technical workflows, validate implementation feasibility, develop prototypes, and turn approved solution concepts into reliable
applications using modern software-engineering practices, Microsoft Azure services, enterprise integrations, and fit-for-purpose AI capabilities.
The role requires deep hands-on capability in four core areas: Python application engineering, production GenAI and RAG development, Microsoft Azure application development, and enterprise API and integration engineering. Supporting disciplines such as CI/CD, observability, cloud networking, and application security require practical implementation knowledge, but not specialist-level mastery.
The engineer will partner with Principal AI Solutions Architect when build-level technical validation is needed during solution shaping. The Principal Solutions Architect owns client discovery, pre-sales strategy, overall solution architecture, scope, and commercial solution integrity; the Senior AI Applications Engineer owns detailed technical design and build-side execution.
Job Description:
• Design, develop, test, deploy, and maintain secure AI and automation applications based on approved solution architectures and business requirements.
• Build generative AI, retrieval-augmented generation, semantic search, intelligent document processing, structured extraction, and workflow-automation solutions.
• Develop tool-enabled and agentic workflows that integrate approved APIs and enterprise systems, maintain workflow state, manage exceptions, and incorporate human approvals.
• Implement prompt orchestration, structured outputs, tool calling, guardrails, confidence thresholds, retry logic, fallback procedures, and audit trails.
• Develop APIs, connectors, integration services, data pipelines, transformations, and validation processes for structured and unstructured enterprise data.
• Prepare documents, metadata, embeddings, indexes, and knowledge sources for retrieval-based applications, and tune retrieval quality using measurable evaluation criteria.
• Integrate applications with enterprise platforms using APIs, webhooks, databases, message queues, and event-driven patterns.
• Build primarily in Microsoft Azure using appropriate AI, application, data, integration, identity, monitoring, and automation services within approved cloud networking and security patterns.
• Deploy applications using containers, serverless services, managed platforms, infrastructure configuration, and CI/CD pipelines.
• Develop automated tests and evaluation methods for functional correctness, groundedness, retrieval quality, reliability, latency, security, and cost.
• Apply security by design throughout development, including secure authentication and authorization, least privilege, encryption, secrets management, input validation, logging, privacy controls, and protected network paths.
• Instrument and monitor production applications for availability, response quality, model or prompt degradation, retrieval failures, integration issues, latency, and cost.
• Diagnose and resolve defects involving application code, APIs, data pipelines, prompts, cloud services, permissions, integrations, and source systems.
• Tune prompts, retrieval strategies, workflow logic, thresholds, and application performance based on evaluation results and user feedback.
• Produce technical specifications, code documentation, data mappings, deployment documentation, runbooks, release notes, and knowledge-transfer materials.
• Conduct code reviews and contribute reusable CrossTech components, engineering standards, deployment patterns, and AI accelerators.
• Support selected client discovery or technical validation sessions when deep engineering input is required, advising on feasibility, data readiness, integrations, implementation effort, and delivery risk.
• Build prototypes, demonstrations, and proofs of concept that validate critical technical assumptions and support the proposed solution.
• Provide the Principal AI Solutions Architect with build estimates, technical assumptions, implementation details, and delivery risks for proposals and handoff.
Required Qualifications:
• Bachelor's degree in computer science, engineering, information systems, data science, or a related field, or equivalent professional experience.
• Seven or more years of experience in software engineering, application development, data engineering, AI engineering, or a related discipline.
• Three or more years of combined hands-on experience developing generative AI, machine-learning, intelligent automation, or AI-enabled applications, including recent experience building LLM-powered applications.
• Demonstrated experience personally developing and deploying at least one GenAI, RAG, or intelligent automation application into secure production use.
• Ability to translate business processes and requirements into detailed, secure, and buildable AI workflow architectures.
• Advanced Python proficiency and strong knowledge of application architecture, object-oriented design, source control, testing, code review, and maintainable software-engineering practices.
• Strong experience with SQL, APIs, data pipelines, data transformation, validation, and structured and unstructured data processing.
• Hands-on experience with large language models, prompt engineering, RAG, embeddings, vector search, semantic retrieval, document processing, and tool-enabled workflows.
• Strong production experience with Microsoft Azure and relevant AI, application, data, integration, identity, and monitoring services.
• Experience with Git, automated testing, CI/CD, containers, observability, cloud deployment, and production troubleshooting.
• Security-by-design mindset and practical knowledge of identity and access management, secure coding, data privacy, secrets management, responsible AI controls, and least-privilege access.
• Ability to implement applications within approved cloud networking and security architectures; specialist-level enterprise network architecture experience is not required.
• Strong analytical, documentation, communication, and problem-solving skills, including the ability to collaborate effectively with architects, client stakeholders, and delivery teams.
Preferred Qualifications:
• Candidates with strong AWS application-engineering experience will be considered if they demonstrate transferable cloud skills and the ability to become productive in Azure quickly.
• Experience with Azure OpenAI in Microsoft Foundry, Azure AI Search, Azure Functions, Logic Apps, API Management, Entra ID, Key Vault, Azure Monitor, Microsoft Fabric, or related Microsoft services.
• Experience with comparable AWS services such as Amazon Bedrock, Lambda, Step Functions, OpenSearch, API Gateway, IAM, CloudWatch, or related data and integration services.
• Experience with Power Automate, Copilot Studio, UiPath, or a comparable automation platform.
• Experience with Azure AI Search, pgvector, Pinecone, Weaviate, OpenSearch, Milvus, or comparable vector-search technology.
• Experience with AI evaluation, observability, prompt monitoring, red-team testing, and application-performance testing.
• Experience with cloud network security, including virtual networks, private endpoints, firewalls, secure connectivity, segmentation, and zero-trust principles.
• Experience building intelligent intake, document-processing, customer-service, procurement, quoting, renewal, sales-operations, or back-office automation solutions.
• Experience working in consulting, regulated industries, or client-delivery environments.
• Familiarity with OWASP guidance for web, API, and LLM applications; responsible AI frameworks; or the NIST AI Risk Management Framework.
• Relevant Microsoft Azure software-engineering, AI, data, automation, or security certifications; comparable AWS certifications are also valued.
Success in This Role:
The successful candidate will convert approved AI solution designs into secure, reliable, and maintainable applications. They will deliver working software that meets defined business, quality, security, performance, and
cost requirements while creating reusable engineering practices that strengthen future client's engagements.