We are seeking a visionary AI Architect to lead the design and integration of cutting-edge AI systems, including Generative AI, Large Language Models (LLMs), multi-agent orchestration, and retrieval-augmented generation (RAG) frameworks.

This role demands a strong technical foundation in machine learning, deep learning, and AI infrastructure, along with hands-on experience in building scalable, production-grade AI systems on the cloud.

The ideal candidate combines architectural leadership with hands-on proficiency in modern AI frameworks, and can translate complex business goals into innovative, AI-driven technical solutions.

Primary Stack & Tools :

- Languages : Python, SQL, Bash.

- ML/AI Frameworks : PyTorch, TensorFlow, Scikit-learn, Hugging Face Transformers.

- GenAI & LLM Tooling : OpenAI APIs, LangChain, LlamaIndex, Cohere, Claude, Azure OpenAI.

- Agentic & Multi-Agent Frameworks : LangGraph, CrewAI, Agno, AutoGen.

- Search & Retrieval : FAISS, Pinecone, Weaviate, Elasticsearch.

- Cloud Platforms : AWS, GCP, Azure (preferred : Vertex AI, SageMaker, Bedrock).

- MLOps & DevOps : MLflow, Kubeflow, Docker, Kubernetes, CI/CD pipelines, Terraform, FAST API.

- Data Tools : Snowflake, BigQuery, Spark, Airflow.

Key Responsibilities :

- Architect scalable and secure AI systems leveraging LLMs, GenAI, and multi-agent frameworks to support diverse enterprise use cases (e.g., automation, personalization, intelligent search).

- Design and oversee implementation of retrieval-augmented generation (RAG) pipelines integrating vector databases, LLMs, and proprietary knowledge bases.

- Build robust agentic workflows using tools like LangGraph, CrewAI, or Agno, enabling autonomous task execution, planning, memory, and tool use.

- Collaborate with product, engineering, and data teams to translate business requirements into architectural blueprints and technical roadmaps.

- Define and enforce AI/ML infrastructure best practices, including security, scalability, observability, and model governance.

- Manage technical road-map, sprint cadence, and 35 AI engineers; coach on best practices.

- Lead AI solution design reviews and ensure alignment with compliance, ethics, and responsible AI standards.

- Evaluate emerging GenAI & agentic tools; run proofs-of-concept and guide build-vs-buy decisions.

Qualifications :

- 10+ years of experience in AI/ML engineering or data science, with 3+ years in AI architecture or system design.

- Proven experience designing and deploying LLM-based solutions at scale, including fine-tuning, prompt engineering, and RAG-based systems.

- Strong understanding of agentic AI design principles, multi-agent orchestration, and tool-augmented LLMs.

- Proficiency with cloud-native ML/AI services and infrastructure design across AWS, GCP, or Azure.

- Deep expertise in model lifecycle management, MLOps, and deployment workflows (batch, real-time, streaming).

- Familiarity with data governance, AI ethics, and security considerations in production-grade systems.

- Excellent communication and leadership skills, with the ability to influence technical and business stakeholders.