AI Architect

Life at EvoluteIQ

We at EvoluteIQ believe in the power of transformation. We are committed to building an industry leading technology that will revolutionize the way enterprises conduct business. To make that happen, we need people who are generous, genuine, self-driven, and collaborative.

People who not only want to be a part of a fast-growing and radical thinking company, but who are kind and caring—about each other. We at EvoluteIQ thrive in the company of each other and make each other a better version of ourselves every day.

Could that be you?

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.

What you'll do at EvoluteIQ :

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.

What will you bring to the team?

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