Position : Machine Learning (ML) Architect

Experience : 5+ Years

Employment Type : Full-Time / Permanent

Job Overview :

We are seeking a Machine Learning Architect to design and lead the end-to-end architecture of scalable ML systemscovering data ingestion, feature engineering, model training, deployment, and continuous monitoring. The ideal candidate will bring deep expertise in MLOps, deep learning, and cloud-native ML platforms, enabling high-performing and secure AI solutions that align with strategic business objectives.

Key Responsibilities :

- Architect End-to-End ML Systems : Design scalable ML pipelines including data ingestion, preprocessing, model training, and inference workflows.

- Implement MLOps Practices : Define CI/CD, automated retraining, version control, and model governance frameworks for efficient ML lifecycle management.

- Deep Learning Architecture : Design and optimize deep learning solutions for complex data types, including audio, video, and multimodal inputs.

- GenAI and LLM Integration : Architect and deploy Generative AI and Large Language Model (LLM)-based solutions using modern frameworks.

- Cloud and Infrastructure : Lead deployment on AWS, Azure, or GCP using Kubernetes, Kubeflow, SageMaker, Vertex AI, or Azure ML.

- Cross-Functional Collaboration : Partner with data scientists, ML engineers, data engineers, and DevOps teams to ensure seamless integration of ML systems.

- Performance, Compliance, and Security : Ensure solutions meet organizational standards for security, scalability, and compliance.

- Technology Evaluation : Continuously evaluate new ML frameworks, tools, and best practices to enhance architecture efficiency and innovation.

Required Qualifications :

- Bachelors or Masters in Computer Science, Artificial Intelligence, Data Science, or a related field.

- 5+ years of experience in designing and implementing ML/AI architectures and MLOps frameworks.

- Proven expertise in Deep Learning (CNNs, RNNs, Transformers), Generative AI, and LLM-based architectures.

- Proficiency in Python, TensorFlow, PyTorch, and MLFlow or equivalent tools.

- Strong knowledge of cloud-native ML ecosystems (AWS SageMaker, Azure ML, GCP Vertex AI).

- Solid understanding of data engineering, CI/CD pipelines, and container orchestration (Docker, Kubernetes).

- Experience with monitoring and observability tools for ML systems (e.g., Prometheus, Grafana, EvidentlyAI).

- Excellent problem-solving, communication, and leadership skills.

Preferred Skills :

- Experience with distributed training frameworks such as Horovod or DeepSpeed.

- Familiarity with vector databases (Pinecone, FAISS) and retrieval-augmented generation (RAG) systems.

- Knowledge of data governance and ML compliance frameworks.

- Hands-on experience in AI model optimization for edge or real-time environments.

Why Join Us :

- Work on cutting-edge ML and AI innovations across multiple domains.

- Collaborate with an exceptional team of data scientists and engineers.

- Opportunity to shape the organizations AI and ML roadmap.

- Competitive compensation with strong growth opportunities.