permanent work from HomeWork Timing -4 PM -1 Am /Hybrid Hyderabad Role Overview
We are seeking a hands-on, full-stack AI Engineer who thrives in fast iteration loops and wants to design, build, and operate intelligent AI solutions at scale. You will work shoulder to shoulder with cross-functional development teams to build GenAI and agentic AI applications for enterprise use cases — from rapid proofs of concept (POCs) through MVPs to scaled production deployments. Proven experience building and deploying AI products is required; Travel and Hospitality experience is a plus.
LLM Application Engineering
- Own LLM application engineering as a core technical discipline, including prompting, RAG, tool use, evaluation, guardrails, and orchestration — driving iterative optimization in partnership with product teams.
- Build, fine-tune, and evaluate LLM-based applications for internal and customer-facing use cases, spanning retrieval-augmented generation, function calling, tool use, multi-turn workflows, and guardrails.
- Design and implement agentic workflows where they add clear value — including tool use, multi-step execution, and human-in-the-loop controls — with attention to reliability, safety, and well-defined failure modes.
- Build robust agent capabilities including context engineering, memory and state management (short-term and long-term), orchestration, routing, and tool integration patterns.
- Build task-oriented AI agents and automation workflows with human-in-the-loop controls, safety constraints, and full auditability.
- Design and implement pipelines for AI response enforcement, content safety, and output formatting.
AI Platform & Solution Engineering
- Design and implement AI/ML solutions using Azure Machine Learning, Azure AI Foundry (AI Studio), OpenAI on Azure — delivering resilient, observable, and cost-optimized applications.
- Define the technical direction and long-term roadmap for internal AI platforms and tooling; architect and lead full-stack AI application development across diverse company use cases.
- Architect distributed systems to ensure high availability, low latency, and fault tolerance; leverage Azure services to build cloud-native solutions.
- Build and maintain production-grade integrations connecting AI models with internal tools, data sources, and enterprise workflows.
- Integrate AI into Power Platform solutions and line-of-business apps using tools and services such as Copilot Studio, Azure Cognitive Services, and enterprise connectors.
- Design context management patterns and integrate enterprise data sources such as Fabric OneLake, Synapse, Microsoft Graph, etc
Data, ML & Model Engineering
- Execute training runs, ablations, evaluations, and model experiments; own model codebases covering data loaders, training loops, evaluation harnesses, and inference tooling.
- Optimize model performance across compute, memory, and distributed training dimensions.
- Develop and maintain pipelines and ML models; implement robust feature engineering and model monitoring across the full ML lifecycle.
- Build ML solutions end-to-end: data preparation, feature engineering, model selection, 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, experiment tracking, robust validation, and clear documentation.
- Design and build advanced search, retrieval, and knowledge pipelines across diverse 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, environment parity, and model, prompt, and agent versioning for operational readiness.
- Build CI/CD pipelines for models and prompts using Git, GitHub, and Azure DevOps; manage environment provisioning, automated tests, A/B and canary deployments, and rollbacks.
- Evolve production monitoring and regression testing for inference quality, cost, and latency, driving iterative improvements post-release.
- Build evaluation and observability for GenAI and agentic systems: tracing and instrumentation, regression test suites, automated scoring, and prompt and policy optimization loops.
- Package models for production and collaborate with deployment engineers and operations 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 and vulnerability remediation.
- Design for secure enterprise deployment: access controls, auditability, data handling 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 IT governance requirements.
- Define and oversee evaluation frameworks for AI-powered features, ensuring inference quality, safety, and alignment with organizational standards.
Stakeholder Collaboration & Technical Leadership
- Partner with business stakeholders to translate product vision into technical and data requirements for AI-powered solutions — advising on what is achievable, what is risky, and what requires further investigation.
- Collaborate cross-functionally with frontend engineers, product managers, IT infrastructure, security, and operations teams to align on technical solutions.
- Communicate clearly with technical and non-technical stakeholders; lead working sessions, present recommendations, and write crisp technical documentation.
- Establish engineering best practices, design patterns, and quality standards for AI systems development across the team.
- Mentor and guide engineers contributing to AI initiatives, fostering a culture of technical excellence.
- Maintain hands-on involvement through prototyping, proofs of concept, and direct contribution to critical implementations.
- Support proposal shaping and scoping: effort sizing, architecture options, risk assessment, and delivery roadmaps.
- Create runbooks, model cards, data contracts, and playbooks; enable developers and users on safe and effective AI use.
Innovation & Continuous Improvement
- Evaluate emerging technologies and drive adoption of best-in-class tools and frameworks, incorporating their capabilities into the platform.
- Contribute to AI excellence by developing reference implementations, documentation, and best practices, while tracking the evolving AI landscape and identifying the right moments to introduce new capabilities.
- Build reusable components and accelerators — including templates, evaluation harnesses, connectors, and orchestration patterns — that scale across multiple product and client contexts.
- Drive code automation practices across the team to ensure maintainability and extensibility.
- Rapidly iterate on AI tooling as the technology landscape and business needs evolve
Pay: ₹500,000.00 - ₹1,500,000.00 per year
Benefits:
Application Question(s):
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Are you ok with work timings 4 pm to 1 am (Midnight )permanent work from Home ?
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How much notice period do you have ?
- What is your expected CTC ?
Experience:
- AI Foundry: 3 years (Required)
- Azure: 5 years (Required)
- AI: 5 years (Preferred)
Work Location: Remote
