About the role
As an AI Tech Lead, you will be responsible for designing, developing, and deploying advanced AI and Machine Learning solutions on the Azure platform. You will lead technical architecture, mentor AI/ML engineers, and drive the successful execution of complex AI initiatives from concept to production.
Responsibilities:
- Define and implement scalable and secure AI architectures on Microsoft Azure.
- Lead end-to-end AI/ML solution development and deployment.
- Mentor and guide AI engineers through code reviews and technical leadership.
- Implement MLOps/LLMOps best practices and CI/CD pipelines.
- Work on Generative AI, Large Language Models (LLMs), and Agentic AI solutions.
- Collaborate with cross-functional teams to deliver impactful AI solutions.
- Establish AI governance, optimisation, and responsible AI practices.
The core requirements for the job include the following:
Languages and Frameworks:
- Strong expertise in Python and AI/ML libraries.
- Hands-on experience with PyTorch, TensorFlow, and JAX.
- Experience with HuggingFace Transformers and Diffusers ecosystem.
- Expertise in LangChain, LangGraph, LlamaIndex, and Semantic Kernel.
- Practical exposure to MLflow, Kubeflow, and Weights and Biases.
Generative AI and LLMs:
- Strong experience with RAG architectures and Vector Databases (FAISS, Pinecone, Weaviate, Milvus).
- Experience with Multi-Agent orchestration and OpenAI APIs.
- Expertise in LLM fine-tuning techniques such as LoRA and QLoRA.
- Advanced Prompt Engineering and AI safety alignment knowledge.
Machine Learning and Deep Learning:
- Strong foundation in Classical ML and Deep Learning models.
- Experience with CNNs, Transformers, Time Series Forecasting, and Graph Neural Networks.
- Exposure to Reinforcement Learning concepts and architectures.
Computer Vision:
- Experience with YOLO, DETR, Faster R-CNN, OCR frameworks, and Video Analytics
- Familiarity with Multi-modal AI models like CLIP, BLIP, and Segment Anything (SAM)
Optimisation and Deployment:
- Expertise in TensorRT, ONNX Runtime, Quantisation, and Model Optimisation.
- Experience deploying Dockerized microservices on Kubernetes.
- Deep expertise in Azure AI/ML services, including AKS, Azure OpenAI, AML, Azure Functions, etc.
- Familiarity with AWS SageMaker and GCP Vertex AI is an added advantage.
- Experience with monitoring tools such as Prometheus, Grafana, and ELK Stack.
Qualifications:
- Bachelor's/Master's degree in Computer Science or related field.
- 6+ years of experience in AI/ML with proven production deployment experience.
- Strong understanding of ML/DL concepts, neural networks, and optimisation techniques.
- Excellent analytical, problem-solving, and debugging skills.
- Open-source contributions, publications, or patents will be an added advantage.