Principal AI Architect

Company Description

EvoluteIQ builds the future of autonomous enterprise by combining GenAI, automation, and advanced orchestration. Our platform empowers organizations to solve complex operational challenges and accelerate digital transformation.

EvoluteIQ is the only AI-native , end-to-end automation platform that looks at the whole process , not just its parts.

Join a company where technology meets vision, and ambitious ideas become powerful realities.

Role Description

EvoluteIQ is seeking a Principal AI Architect with deep expertise in advanced machine learning, AI frameworks. The candidate will lead end-to-end AI solution design, build scalable enterprise-grade products, and mentor cross-functional teams. Strong strategic vision and up-to-date knowledge of AI trends are essential to align technology with business goals.

Key Responsibilities :

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.

Required Experience :

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 MLOpstools 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).

Good 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