About the role
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.