Job Summary :

We are seeking a forward-thinking AI Architect to design, lead, and scale enterprise-grade AI systems and solutions across domains. This role demands deep expertise in machine learning, generative AI, data engineering, cloud-native architecture, and orchestration frameworks. You will collaborate with cross-functional teams to translate business requirements into intelligent, production-ready AI solutions.

Key Responsibilities :

Architecture & Strategy :

- Design end-to-end AI architectures that include data pipelines, model development, MLOps, and inference serving.

- Create scalable, reusable, and modular AI components for different use cases (vision, NLP, time series, etc.).

- Drive architecture decisions across AI solutions, including multi-modal models, LLMs, and agentic workflows.

- Ensure interoperability of AI systems across cloud (AWS/GCP/Azure), edge, and hybrid environments.

Technical Leadership :

- Guide teams in selecting appropriate models (traditional ML, deep learning, transformers, etc.) and technologies.

- Lead architectural reviews and ensure compliance with security, performance, and governance policies.

- Mentor engineering and data science teams in best practices for AI/ML, GenAI, and MLOps.

Model Lifecycle & Engineering :

- Oversee implementation of model lifecycle using CI/CD for ML (MLOps) and/or LLMOps workflows.

- Define architecture for Retrieval Augmented Generation (RAG), vector databases, embeddings, prompt engineering, etc.

- Design pipelines for fine-tuning, evaluation, monitoring, and retraining of models.

Data & Infrastructure :

- Collaborate with data engineers to ensure data quality, feature pipelines, and scalable data stores.

- Architect systems for synthetic data generation, augmentation, and real-time streaming inputs.

- Define solutions leveraging data lakes, data warehouses, and graph databases.

Client Engagement / Product Integration :

- Interface with business/product stakeholders to align AI strategy with KPIs.

- Collaborate with DevOps teams to integrate models into products via APIs/microservices.

Required Skills & Experience :

Core Skills :

- Strong foundation in AI/ML/DL (Scikit-learn, TensorFlow, PyTorch, Transformers, Langchain, etc.)

- Advanced knowledge of Generative AI (LLMs, diffusion models, multimodal models, etc.)

- Proficiency in cloud-native architectures (AWS/GCP/Azure) and containerization (Docker, Kubernetes)

- Experience with orchestration frameworks (Airflow, Ray, LangGraph, or similar)

- Familiarity with vector databases (Weaviate, Pinecone, FAISS), LLMOps platforms, and RAG design

Architecture & Programming :

- Solid experience in architectural patterns (microservices, event-driven, serverless)

- Proficient in Python and optionally Java/Go

- Knowledge of APIs (REST, GraphQL), streaming (Kafka), and observability tooling (Prometheus, ELK, Grafana)

Tools & Platforms :

- ML lifecycle tools: MLflow, Kubeflow, Vertex AI, Sagemaker, Hugging Face, etc.

- Prompt orchestration tools: LangChain, CrewAI, Semantic Kernel, DSPy (nice to have)

- Knowledge of security, privacy, and compliance (GDPR, SOC2, HIPAA, etc.)