Linnk Group is hiring Senior Node.js Developer and Mid Senior Node.js Developers to take ownership of backend systems, mentor growing teams, and help shape the future of scalable tech solutions — all from our vibrant office in Cochin, India. Position: Senior/Mid Senior Node.js Developer Location: Cochin, India (On-site only) Job Type: Full-Time Experience: 3–10 Years Key Responsibilities Lead backend development using Node.js for complex, scalable applications Design and architect RESTful and GraphQL APIs Drive optimization, security, and reliability across backend systems Work extensively with PostgreSQL and MongoDB Implement CI/CD pipelines and Docker-based deployments Mentor junior/mid-level developers and lead code reviews Collaborate with cross-functional teams including DevOps, QA, and Product Required Experience 6–10 years of professional experience in backend engineering Deep expertise in JavaScript (ES6+), Express.js, NestJS Proven track record in designing and maintaining microservices architecture Strong experience with database optimization, caching strategies Skilled in Docker, Git, and deployment automation Strong understanding of software security, performance tuning, and scalability Prior experience in technical mentorship or leading backend teams Bonus Skills TypeScript proficiency Experience with messaging systems like RabbitMQ, Kafka Exposure to Agile (Scrum/Kanban) Familiarity with frontend technologies (React, Angular) is a plus What We Offer Competitive salary based on experience and skill Leadership role in high-impact, enterprise-level projects Collaborative, forward-thinking team culture Continuous learning and growth opportunities A chance to influence technical direction at Linnk Group On-site role at our growing tech hub in Cochin Kr, Show more Show less

Job Title: AI Lead – Generative AI & ML Systems Key Responsibilities Generative AI Development Design and implement LLM-powered solutions and generative AI models for use cases such as predictive analytics, automation workflows, anomaly detection, and intelligent systems. · RAG & LLM Applications Build and deploy Retrieval-Augmented Generation (RAG) pipelines, structured generation systems, and chat-based assistants tailored to business operations. Full AI Lifecycle Management Lead the complete AI lifecycle—from data ingestion and preprocessing to model design, training, testing, deployment, and continuous monitoring. · Optimization & Scalability Develop high-performance AI/LLM inference pipelines, applying techniques like quantization, pruning, batching, and model distillation to support real-time and memory-constrained environments. MLOps & CI/CD Automation Automate training and deployment workflows using Terraform, GitLab CI, GitHub Actions, or Jenkins, integrating model versioning, drift detection, and compliance monitoring. Cloud & Deployment Deploy and manage AI solutions using AWS, Azure, or GCP with containerization tools like Docker and Kubernetes. AI Governance & Compliance Ensure model/data governance and adherence to regulatory and ethical standards in production AI deployments. Stakeholder Collaboration Work cross-functionally with product managers, data scientists, and engineering teams to align AI outputs with real-world business goals. Required Skills & Qualifications Bachelor’s degree (B.Tech or higher) in Computer Science, IT, or a related field is required. 8-12 Year exp- from the Ai team with overall experience in Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) solution development. Minimum 2+ years of hands-on experience in Generative AI and LLM-based solutions, including prompt engineering, fine-tuning, Retrieval-Augmented Generation (RAG) pipelines with full CI/CD integration, monitoring, and observability pipelines, with 100% independent contribution. Proven expertise in both open-source and proprietary Large Language Models (LLMs), including LLaMA, Mistral, Qwen, GPT, Claude, and BERT. Expertise in C/C++ & Python programming with relevant ML/DL libraries including TensorFlow, PyTorch, and Hugging Face Transformers. Experience deploying scalable AI systems in containerized environments using Docker and Kubernetes. Deep understanding of the MLOps/LLMOps lifecycle, including model versioning, deployment automation, performance monitoring, and drift detection. Familiarity with CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins) and DevOps for ML workflows. Working knowledge of Infrastructure-as-Code (IaC) tools like Terraform for cloud resource provisioning and reproducible ML pipelines. Hands-on experience with cloud platforms (AWS, GCP, Azure) and container orchestration (Docker, Kubernetes). Designed and documented High-Level Design (HLD) and Low-Level Design (LLD) for ML/GenAI systems, covering data pipelines, model serving, vector search, and observability layers. Documentation included component diagrams, network architecture, CI/CD workflows, and tabulated system designs. Provisioned and managed ML infrastructure using Terraform, including compute clusters, vector databases, and LLM inference endpoints across AWS, GCP, and Azure. Experience beyond notebooks: shipped models with logging, tracing, rollback mechanisms, and cost control strategies. Hands-on ownership of production-grade LLM workflows, not limited to experimentation. Full CI/CD integration, monitoring, and observability pipelines, with 100% independent contribution. Preferred Qualifications (Good To Have) Experience with LangChain, LlamaIndex, AutoGen, CrewAI, OpenAI APIs, or building modular LLM agent workflows. Exposure to multi-agent orchestration, tool-augmented reasoning, or Autonomous AI agents and agentic communication patterns with orchestration. Experience deploying ML/GenAI systems in regulated environments, with established governance, compliance, and Responsible AI frameworks. Familiarity with AWS data and machine learning services, including Amazon SageMaker, AWS Bedrock, ECS/EKS, and AWS Glue, for building scalable, secure data pipelines and deploying end-to-end AI/ML workflows.