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.