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Working here means having every opportunity to achieve your goals - and to help others accomplish theirs, too. Join our team as we help shape the future.\nPosition Summary\nWe are seeking a highly skilled T7 AI Engineer to join our engineering team in Hyderabad, India. This role combines hands-on AI/ML engineering with deep software development expertise to build, deploy, and operate production-grade AI systems at enterprise scale. You will design and implement AI-powered solutions - from LLM integrations and agentic workflows to ML pipelines and intelligent automation - while driving AI adoption and engineering excellence across teams.\nLevel: T7 (Senior Engineer)\nLocation: Hyderabad, India\nEmployment Type: Full-Time\nKey Responsibilities\nAI/ML Engineering & Delivery\nDesign, build, and deploy production AI systems including RAG pipelines, agentic workflows, multi-model orchestration, and intelligent automation\n Integrate large language model (LLM) APIs and AI/ML services into enterprise applications (GCP Vertex AI)\nImplement and optimize prompt engineering strategies, fine-tuning pipelines, embeddings, and vector search solutions\nBuild and maintain AI orchestration workflows using frameworks such as LangChain, Semantic Kernel, AutoGen, CrewAI, or LlamaIndex\nDevelop custom AI agents, tools, and autonomous workflows that solve real business problems\nEstablish evaluation frameworks for AI systems - measuring accuracy, latency, cost, hallucination rates, and business outcomes\nFull Stack Development & Integration\nBuild end-to-end AI-powered applications spanning frontend, backend, APIs, and data layers\n Develop robust backend services using Python (FastAPI/Django) or Node.js to support AI workloads\nImplement and optimize RESTful APIs, GraphQL endpoints, and event-driven integrations for AI services\nBuild modern frontend interfaces for AI-powered features using React, Angular, or Vue.js with TypeScript\nWrite clean, well-tested, production-ready code with a focus on maintainability and operational excellence\nMLOps & AI Infrastructure\nDesign and implement MLOps/LLMOps pipelines for reliable model deployment, versioning, and lifecycle management\n Configure and manage cloud-native AI infrastructure (AWS, GCP) including model serving, orchestration, and auto-scaling\nImplement observability for AI systems - monitoring model drift, token costs, latency, throughput, and quality metrics\nBuild and maintain CI/CD pipelines for AI model deployment, automated testing, and continuous evaluation\nDesign for resilience: failover strategies, fallback models, circuit breakers, and graceful degradation\nAI-Augmented Development\n Leverage AI coding assistants (GitHub Copilot, Cursor, Claude, etc.) to dramatically accelerate development workflows\nUse AI tools for code generation, refactoring, test writing, documentation, and code review\nDevelop and maintain custom AI-powered developer tools, automations, and internal platforms\nEstablish guardrails, security practices, and governance for responsible AI usage in engineering\nTechnical Documentation & Mentorship\nInfluence engineering culture by evangelizing AI-first development practices across teams\nTrain and upskill team members on effective use of AI tools, LLM integration patterns, and ML best practices\nContribute to internal knowledge bases, tech talks, and communities of practice\nPartner with product, design, and data science teams to identify and deliver AI-driven opportunities\nParticipate in architecture reviews and design discussions, ensuring AI solutions are production-ready from day one\nRequired Qualifications\nExperience: 8+ years of professional software engineering experience, with 2+ years focused on AI/ML solution development and delivery\n Education: Bachelor's degree in Computer Science, Software Engineering, AI/ML, or related field (or equivalent experience)\nAI/ML Expertise:Strong understanding of large language model architectures, capabilities, and limitations\n Proven track record building and deploying production AI systems (RAG, agents, fine-tuning, embeddings, vector search)\n Proficiency with AI orchestration frameworks (LangChain, Semantic Kernel, AutoGen, CrewAI, LlamaIndex)\n Hands-on experience with major LLM providers and platforms (OpenAI, Anthropic, Google Vertex AI, AWS Bedrock)\nSolid understanding of prompt engineering, evaluation methodologies, and AI safety/guardrails\n\n Programming: Expert-level proficiency in Python; strong skills in at least one additional language (Java, TypeScript/Node.js, C#/.NET)\n Cloud & Infrastructure: Hands-on experience deploying and operating AI workloads on cloud platforms (AWS, GCP, or Azure), containerization (Docker, Kubernetes), and CI/CD tooling (Jenkins, GitHub Actions)\n Data: Proficiency with SQL and NoSQL databases, vector databases (Pinecone, Weaviate, pgvector, ChromaDB), and data pipeline tools\n API Development: Proven track record building production APIs (REST, GraphQL, gRPC) and event-driven integrations\nAI Tools Proficiency: Advanced daily usage of AI coding assistants with demonstrated impact on productivity and code quality\nTesting: Strong testing practices for AI systems including model evaluation, integration testing, and automated quality checks\nCommunication: Excellent written and verbal communication skills with ability to explain complex AI concepts to diverse audiences\nPreferred Qualifications\nExperience building enterprise AI platforms serving multiple product teams\n Familiarity with custom model training, fine-tuning (LoRA, QLoRA), and RLHF techniques\nExperience with agent-based AI architectures and autonomous multi-step workflows\n Knowledge of AI security concerns (prompt injection, data leakage, model poisoning) and mitigation strategies\n Experience in regulated industries (insurance, finance, healthcare) with security and compliance requirements\nContributions to open-source AI/ML projects or published technical content\n Cloud certifications (AWS Solutions Architect, GCP Professional Cloud Architect, or equivalent)\nExperience with real-time inference, streaming responses, and low-latency AI serving architectures\nAbout Us | Our Culture | What It’s Like to Work 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