permanent work from HomeWork Timing -4 PM -1 Am AI Architect / EngineerRole OverviewWe are seeking a hands-on, full-stack AI Engineer who thrives in fast iteration loops andwants to design, build, and operate intelligent AI solutions at scale. You will work shoulderto shoulder with cross-functional development teams to build GenAI and agentic AIapplications for enterprise use cases — from rapid proofs of concept (POCs) through MVPsto scaled production deployments. Proven experience building and deploying AI productsis required; Travel and Hospitality experience is a plus.LLM Application Engineering Own LLM application engineering as a core technical discipline, includingprompting, RAG, tool use, evaluation, guardrails, and orchestration — drivingiterative optimization in partnership with product teams. Build, fine-tune, and evaluate LLM-based applications for internal and customerfacing use cases, spanning retrieval-augmented generation, function calling, tooluse, multi-turn workflows, and guardrails. Design and implement agentic workflows where they add clear value — includingtool use, multi-step execution, and human-in-the-loop controls — with attention toreliability, safety, and well-defined failure modes. Build robust agent capabilities including context engineering, memory and statemanagement (short-term and long-term), orchestration, routing, and tool integrationpatterns. Build task-oriented AI agents and automation workflows with human-in-the-loopcontrols, safety constraints, and full auditability. Design and implement pipelines for AI response enforcement, content safety, andoutput formatting.AI Platform & Solution Engineering Design and implement AI/ML solutions using Azure Machine Learning, Azure AIFoundry (AI Studio), OpenAI on Azure — delivering resilient, observable, and costoptimized applications. Define the technical direction and long-term roadmap for internal AI platforms andtooling; architect and lead full-stack AI application development across diversecompany use cases. Architect distributed systems to ensure high availability, low latency, and faulttolerance; leverage Azure services to build cloud-native solutions. Build and maintain production-grade integrations connecting AI models withinternal tools, data sources, and enterprise workflows. Integrate AI into Power Platform solutions and line-of-business apps using tools andservices such as Copilot Studio, Azure Cognitive Services, and enterpriseconnectors. Design context management patterns and integrate enterprise data sources such asFabric OneLake, Synapse, Microsoft Graph, etcData, ML & Model Engineering Execute training runs, ablations, evaluations, and model experiments; own modelcodebases covering data loaders, training loops, evaluation harnesses, andinference tooling. Optimize model performance across compute, memory, and distributed trainingdimensions. Develop and maintain pipelines and ML models; implement robust featureengineering and model monitoring across the full ML lifecycle. Build ML solutions end-to-end: data preparation, feature engineering, modelselection, training, validation and testing, and performance analysis. Partner with the Platform Engineer on dataset creation, feature and data contracts,and pipelines. Create reproducible training and evaluation pipelines with versioning, experimenttracking, robust validation, and clear documentation. Design and build advanced search, retrieval, and knowledge pipelines acrossdiverse data structures — including hybrid search, vector stores, graph databases,and traditional data platforms. Define indexing strategies, metadata design, relevance tuning and reranking,caching, freshness, access controls, and source attribution.MLOps, DevOps & Production Delivery Write clean, testable, and maintainable code; ship AI services through the full SDLC— build, test, deploy, monitor, and iterate. Implement MLOps and GenAIOps practices: CI/CD, reproducibility, environmentparity, and model, prompt, and agent versioning for operational readiness. Build CI/CD pipelines for models and prompts using Git, GitHub, and AzureDevOps; manage environment provisioning, automated tests, A/B and canarydeployments, and rollbacks. Evolve production monitoring and regression testing for inference quality, cost, andlatency, driving iterative improvements post-release. Build evaluation and observability for GenAI and agentic systems: tracing andinstrumentation, regression test suites, automated scoring, and prompt and policyoptimization loops. Package models for production and collaborate with deployment engineers andoperations teams to iteratively improve performance.Security, Governance & Responsible AI Enforce security best practices across the codebase and Azure infrastructure,implementing defense-in-depth strategies and driving timely risk mitigation andvulnerability remediation. Design for secure enterprise deployment: access controls, auditability, datahandling for sensitive and PII data, and responsible AI guardrails. Implement telemetry (App Insights, Prometheus, etc), responsible AI evaluations(fairness, safety, toxicity), RBAC, data classification, and evidence trails aligned to ITgovernance requirements. Define and oversee evaluation frameworks for AI-powered features, ensuringinference quality, safety, and alignment with organizational standards.Stakeholder Collaboration & Technical Leadership Partner with business stakeholders to translate product vision into technical anddata requirements for AI-powered solutions — advising on what is achievable, whatis risky, and what requires further investigation. Collaborate cross-functionally with frontend engineers, product managers, ITinfrastructure, security, and operations teams to align on technical solutions. Communicate clearly with technical and non-technical stakeholders; lead workingsessions, present recommendations, and write crisp technical documentation. Establish engineering best practices, design patterns, and quality standards for AIsystems development across the team. Mentor and guide engineers contributing to AI initiatives, fostering a culture oftechnical excellence. Maintain hands-on involvement through prototyping, proofs of concept, and directcontribution to critical implementations. Support proposal shaping and scoping: effort sizing, architecture options, riskassessment, and delivery roadmaps. Create runbooks, model cards, data contracts, and playbooks; enable developersand users on safe and effective AI use.Innovation & Continuous Improvement Evaluate emerging technologies and drive adoption of best-in-class tools andframeworks, incorporating their capabilities into the platform. Contribute to AI excellence by developing reference implementations,documentation, and best practices, while tracking the evolving AI landscape andidentifying the right moments to introduce new capabilities. Build reusable components and accelerators — including templates, evaluationharnesses, connectors, and orchestration patterns — that scale across multipleproduct and client contexts. Drive code automation practices across the team to ensure maintainability andextensibility. Rapidly iterate on AI tooling as the technology landscape and business needs evolve
Pay: ₹500,000.00 - ₹2,000,000.00 per year
Benefits:
Application Question(s):
- Are you ok with work timings 4 pm to 1 am (Midnight )permanent work from Home ?
- How much notice period do you have ?
- What is your expected CTC ?
Experience:
- AI/ML: 5 years (Required)
- Azure AI Foundry: 2 years (Required)
Work Location: Remote
