AI Tech Lead

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.

Good to Have:

• 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.

Skills

AKS, Azure, Azure ML, Azure OpenAI, Kubeflow, LLMOps, MLOps, MLflow, Microsoft Azure, Python