AI/ML/GenAI Architect

3 weeks ago

New Delhi, India ACL Digital Full time

- 10+ years of progressive experience in designing and implementing enterprise-grade AI/ML solutions.
- Minimum of 3 successful end-to-end project implementations of AI/ML/GenAI solutions in production environments, demonstrating strong business impact and scalability.
- Deep expertise in Generative AI architectures, including but not limited to:
- Large Language Models (LLMs) - prompt engineering, fine-tuning, RAG (Retrieval Augmented Generation) architectures, prompt chaining, agentic workflows.
- Generative Adversative Networks (GANs), Variational Autoencoders (VAEs), Diffusion Models, etc.
- Experience with various LLM frameworks/libraries (e.g., LangChain, LlamaIndex).
- Proven experience with leading cloud AI platforms (at least one of the following):
- AWS: SageMaker, Comprehend, Textract, Polly, Bedrock, etc.
- Azure: Azure Machine Learning, Azure OpenAI Service, Cognitive Services.
- GCP: Vertex AI, Google Cloud AI Platform, Google Cloud Vision/Natural Language AI.
- Strong understanding of core Machine Learning concepts and algorithms: Supervised, Unsupervised, Reinforcement Learning, Deep Learning.
- Proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch, Keras, scikit-learn, Hugging Face Transformers).
- Experience with MLOps principles and tools: CI/CD for ML, model versioning, monitoring, deployment, drift detection (e.g., MLflow, Kubeflow, Sagemaker MLOps services, Azure ML pipelines).
- Solid understanding of data engineering principles relevant to AI/ML workloads (data pipelines, ETL/ELT, data governance, feature stores).
- Strong knowledge of distributed computing frameworks (e.g., Spark, Dask) for large-scale data processing.
- Exceptional communication skills (written and verbal): Ability to articulate complex technical concepts to both technical and non-technical stakeholders, conduct compelling presentations, and influence decision-making.
- Demonstrated experience in leading architectural design discussions and making critical technical decisions.
- Problem-solving mindset with the ability to troubleshoot and resolve complex technical issues in production.

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