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.)

(ref:hirist.tech)

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