Job Title

We are seeking a visionaryAI Architectto lead the design and integration of cutting-edge AI systems, includingGenerative AI ,Large Language Models (LLMs) ,multi-agent orchestration , andretrieval-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, AirflowKey Responsibilities: Architect scalable and secure AI systems leveragingLLMs ,GenAI , andmulti-agent frameworksto support diverse enterprise use cases (e.g., automation, personalization, intelligent search). Design and oversee implementation ofretrieval-augmented generation (RAG)pipelines integrating vector databases, LLMs, and proprietary knowledge bases. Build robustagentic workflowsusing tools likeLangGraph ,CrewAI , orAgno , 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 enforceAI/ML infrastructure best practices , including security, scalability, observability, and model governance. Manage technical road-map, sprint cadence, and 3–5 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 deployingLLM-based solutionsat scale, includingfine-tuning ,prompt engineering , andRAG-based systems . Strong understanding ofagentic AI design principles ,multi-agent orchestration , andtool-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 withdata governance ,AI ethics , andsecurity considerationsin production-grade systems. Excellent communication and leadership skills, with the ability to influence technical and business stakeholders.