2 Llmops Pipelines Jobs

Job Title: Engineering Lead for Building enterprise scale Gen AI Platform Location: [India-Offshore] Experience:11 + Years Employment Type: [Full-Time / Contract] Domain: Generative AI, NLP, LLMs, Full-stack Application development Job Summary: We are seeking a highly skilled Solution Architect with deep knowledge of full-stack technologies and a strong grasp of LLM-based solution design . The ideal candidate will be responsible for architecting and leading scalable, secure, and high-performing GenAI solutions. This role involves close collaboration with product managers, data scientists, backend developers, and domain teams as well as customer stakeholder to design intelligent systems that integrate advanced language models, knowledge graphs, and cloud-native services. You will provide hands-on technical leadership across the full software development lifecycle—from design to delivery—while setting engineering standards and ensuring alignment with enterprise architecture and compliance frameworks. Key Responsibilities: Architect end-to-end GenAI solutions using LLMs (e.g., OpenAI, Deepseek, Llama) for a variety of enterprise use cases including summarization, semantic search, chat agents, and workflow automation. Translate business requirements into scalable LLM-enabled software architectures balancing functional needs with performance, cost, compliance, and user experience requirements. Own full-stack solution delivery — from UX/UI design to backend services, data integrations, and LLM orchestration — ensuring cohesive user experiences and seamless data flow across layers. Guide development teams on prompt engineering , RAG (retrieval-augmented generation) , and LLMOps best practices . Implement robust workflow orchestration for GenAI use cases using tools like Temporal, Airflow, or custom orchestrators to support multi-agent workflows and business logic execution. [RT1] [RT2] Establish secure and compliant LLM pipelines, ensuring adherence to data protection, PII masking, auditability, and enterprise governance policies. Stay abreast of emerging GenAI tools, frameworks, and trends to continuously improve solution design. Lead application development lifecycle : define branching strategies, enforce CI/CD practices, and implement automated quality gates (tests, linting, security scans). Architect and implement self-service internal platforms for build, test, deployment, and monitoring. Drive engineering best practices including clean code, modularization, reusable component libraries, design patterns for GenAI, and consistent coding standards across teams. Maintain architectural oversight across environments, collaborating with DevOps and SRE teams to provision infrastructure using IaC tools (e.g., Terraform, Bicep, Pulumi) and to implement monitoring, logging, and tracing for resilient, observable GenAI platforms. Required Skills: Frontend: React.js, Next.js, or similar frameworks. Backend: Python, Node.js, FastAPI, Flask, Express.js. GenAI Stack: LangChain, LlamaIndex, OpenAI APIs. Cloud Platforms: Azure or GCP (LLM deployments, Kubernetes, serverless functions). Database: PostgreSQL, familiarity with vector databases. Containerization & Orchestration: Docker, Kubernetes, Helm, EKS/GKE/AKS CI/CD: GitHub Actions, Jenkins, ArgoCD, FluxCD Developer Enablement: Backstage, Internal Dev Portals, Platform APIs Experience designing and integrating LLMOps pipelines , prompt templating, and caching strategies. Excellent understanding of API architecture (REST, GraphQL). Proven experience in agile development methodologies . Preferred Qualifications: Experience working with enterprise-scale product or application development, rollout and adoption Hands-on experience with GenAI solution building blocks, LLM fine-tuning, custom embeddings, or proprietary data integration. Contributions to open-source AI/NLP projects or participation in GenAI communities. Exposure to evaluation frameworks for LLM outputs (toxicity, accuracy, hallucination detection). Proven expertise in building internal platforms or PaaS environments . Strong understanding of software engineering practices , microservices, and distributed systems. Passion for automation, standardization, and developer experience .

Role Overview: We are looking for a Lead Data Scientist / AI Solution Architect to join our growing AI & Data team. The ideal candidate will have hands-on experience designing and deploying AI/ML solutions with Azure Cloud , LLMOps , Containerized environments , and Generative AI technologies. This is an onsite role based in Pune , offering you the opportunity to collaborate closely with cross-functional teams on impactful projects. Key Responsibilities: Architect and implement end-to-end AI/ML solutions using Azure AI/ML services Lead the design and development of LLMOps pipelines for deploying and managing large language models (LLMs) Work with containerized deployments using Docker and orchestration tools like Kubernetes Integrate and fine-tune Generative AI models for various real-world applications (text, vision, conversation, etc.) Collaborate with data engineers, product teams, and business stakeholders to deliver scalable AI systems Drive model monitoring, versioning, and responsible AI practices Document technical architecture, decision-making, and knowledge transfer Required Skills & Qualifications: 5 to 7 years of experience in Data Science / AI / ML solution development Strong hands-on experience with Azure AI services (ML Studio, Azure OpenAI, AI Foundry etc.) Experience working with LLMs , prompt engineering, and fine-tuning techniques Proven experience with containerized environments (Docker, Kubernetes) Strong programming skills in Python with relevant ML/AI libraries (TensorFlow, PyTorch, Transformers, etc.) Solid understanding of LLMOps/MLOps lifecycle and best practices Excellent communication, stakeholder management, and team collaboration skills Must Have: Hands-On LangChain, Hugging Face APIs, or Azure OpenAI APIs Strong knowledge of Azure Cognitive Services, Machine Learning Studio, Azure AI Search, AI Foundry Strong Knowledge of basic hygiene principles Git, Clean Code, Unit Testing, Jira, DevOps etc. Good to Have: Experience with Databricks Familiarity with Azure CI/CD pipelines Prior experience working in a startup or fast-paced product environmentRole & responsibilities