Azure AI Tech Lead

Experience: 3.5 – 7 years
Employment Type: Full-time

Role Overview

As the Azure AI Tech Lead, you will serve as the principal technical authority for designing, developing, and deploying sophisticated AI and machine learning solutions on the Azure platform. You will provide technical leadership and architectural guidance to a team of AI/ML engineers, ensuring the successful delivery of complex projects from proof-of-concept to production.

This role requires hands-on expertise across the AI landscape, from foundational machine learning concepts to the latest advancements in generative AI and MLOps. You will shape technical strategy, drive innovation, and foster a culture of excellence in AI development.

Key Responsibilities

• Technical Leadership & Architecture: Design and implement robust, scalable, and secure AI architectures on Azure using Azure AI/ML, Azure OpenAI, and Azure Cognitive Services.
• Solution Development & Delivery: Lead end-to-end AI/ML projects, including data processing, model training, deployment, and monitoring, while providing guidance on complex implementations.
• Team Mentorship: Mentor junior and mid-level AI engineers through code reviews, pair programming, and knowledge sharing.
• MLOps & Automation: Implement MLOps best practices, including CI/CD pipelines for training, deployment, and monitoring of models.
• Innovation & Research: Stay current with advancements in LLMs, generative AI, and agentic systems, driving adoption of new technologies.
• Cross-Functional Collaboration: Work closely with data scientists, engineers, product managers, and business stakeholders to translate requirements into high-impact AI solutions.
• Governance & Best Practices: Establish and enforce AI development standards, including model governance, responsible AI practices, and performance optimization.

Required Technical Expertise

Languages & Frameworks

• Python (advanced data structures, async programming, multiprocessing)
• Deep learning frameworks: PyTorch, TensorFlow, JAX
• HuggingFace Transformers & Diffusers
• LLM/Agentic frameworks: LangChain, LangGraph, LlamaIndex, Semantic Kernel
• MLOps: MLflow, Weights & Biases, Kubeflow

Generative & Agentic AI

• Retrieval-Augmented Generation (RAG): standard, graph-based, and vector DB integrations (FAISS, Pinecone, Weaviate, Milvus)
• Multi-Agent Orchestration: LangGraph, AutoGen, OpenAI function APIs
• Generative Model Fine-Tuning: Stable Diffusion, Flux.1, ControlNet, DreamBooth
• LLM Fine-Tuning: LoRA, QLoRA, PEFT on Llama3, Mistral, CodeLlama, Azure OpenAI models
• Advanced Prompt Engineering: system prompting, function calling, safety alignment

Machine Learning & Deep Learning

• Classical ML: XGBoost, LightGBM, CatBoost, regression, clustering, PCA, t-SNE, UMAP
• Deep Learning: CNNs (ResNet, EfficientNet), RNNs/LSTMs, GRUs, Transformers (BERT, ViT, GPT)
• Graph ML: GNNs (GraphSAGE, GAT, PyTorch Geometric, DGL)
• Time Series: Prophet, ARIMA, DeepAR, Temporal Fusion Transformer
• Reinforcement Learning: RLHF, PPO, DQN, policy gradients

Computer Vision

• Object detection: YOLO (v5–v8), DETR, Faster R-CNN
• OCR: PaddleOCR, Tesseract, EasyOCR, LayoutLM
• Video analytics: object tracking (DeepSORT, ByteTrack), pipelines for video processing
• Multi-modal ML: CLIP, BLIP, Florence-2, Segment Anything (SAM)

Optimization & Deployment

• Model optimization: TensorRT, ONNX Runtime, quantization (INT8, FP16), pruning, distillation
• Scalable deployment: Dockerized microservices, Kubernetes, REST/gRPC APIs
• Cloud platforms: Azure AI/ML (App Service, Azure OpenAI, AML), familiarity with AWS SageMaker and GCP Vertex AI
• Edge AI: Deployment on Nvidia Jetson or Coral TPU using TensorFlow Lite or CoreML
• Monitoring: Model drift and system health with Prometheus, Grafana, ELK stack

Qualifications

• Master’s or Bachelor’s in Computer Science, Electronics, Instrumentation, or related field
• 3.5–7 years of professional AI/ML experience with end-to-end solution delivery
• Hands-on experience taking ML/LLM solutions from proof-of-concept to scalable production deployment, including monitoring and maintenance
• Strong theoretical foundation in ML/DL (optimization, loss functions, neural network architectures)
• Exceptional analytical, problem-solving, and debugging skills
• Applied research experience, publications, open-source contributions, or patents are a plus

Job Type: Full-time

Pay: ₹90,000.00 - ₹100,000.00 per month

Work Location: In person