AI Lead – Generative AI & ML Systems

Job Title:

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.