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We are seeking an experienced AI Architect to lead the design, implementation, and governance of end-to-end AI/ML solutions across our GenAI powered low-code/AI automation platform. The ideal candidate has a proven track record of building, deploying, and managing scalable AI/ML systems – from data ingestion and feature engineering to model deployment, monitoring, and optimization in production environments.
Job Responsibility:
Architect and oversee AI/ML pipelines covering data collection, preparation, training, validation, and inference
Define and implement scalable AI infrastructure for training, deployment, and continuous integration (MLOps)
Collaborate with data scientists, ML engineers, product manager, and product teams to translate business problems into AI-driven solutions
Establish frameworks for model governance, versioning, reproducibility, and explainability
Integrate models into production systems ensuring low latency, scalability, and reliability
Define data strategy, storage, and access patterns to support AI workloads
Build solutions to monitor model performance, drift, and data quality, implementing continuous retraining strategies
Ensure compliance with ethical AI, data privacy, and security best practices
Mentor AI/ML engineers and contribute to architectural decisions across the AI platform stack
Requirements:
12+ years of experience in data science, ML engineering and AI system architecture
Hands-on experience with Python, TensorFlow, PyTorch, Scikit-learn, spaCy and related AI/ML frameworks
Expertise in MLOps tools such as MLflow, Kubeflow, Vertex AI, or SageMaker
Proficiency in data processing technologies (Spark, Kafka, Airflow) and data modeling
Strong background in deploying models such as APIs or services using Docker, Kubernetes, and REST/gRPC
Experience designing data pipelines and integrating AI with production systems
Should have an understanding of prompt engineering, LLM fine-tuning, and vector stores (e.g. Pinecone, FAISS, Weaviate)
Knowledge of cloud AI services (AWS, GCP, Azure) and distributed computing architectures
Proven experience implementing observability for models (drift, accuracy, bias, and performance)
Nice to have:
Experience in architecting AI/ML components for low-code/no-code or automation platforms
Exposure to GenAI, agentic systems, and conversational AI deployment pipelines
Knowledge of compliance frameworks like SOC2, GDPR, and Responsible AI principles
Contributions to open-source AI or ML tooling projects
What we offer:
Opportunity to shape the strategy of a next-gen hyper-automation platform
Work with a cross-disciplinary team in a fast-growing, innovation-driven environment
Competitive compensation and growth opportunities
A culture of innovation, ownership, and continuous learning
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