AI Lead

Job Title: AI Lead Generative AI & ML Systems

Location:

About Us:

Linnk Group

We are looking for a hands-on and experienced Lead AI/ML Engineer to architect, build, and scale our GenAI systems across products. This is a foundational role that will help define the technical direction and lead the implementation of production-grade AI components.

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.