T
AI Architect
T
AI Architect
Tata Consultancy Services LimitedEarly Applicant
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Job Description
- Hands-on experience with Python preferred, plus Java or .NET; strong software engineering knowledge and familiarity with design patterns.
- Experience building LLM solutions, including RAG pipelines, prompt engineering, agent frameworks such as LangChain or LlamaIndex, and evaluation using gold sets, metrics, and red-teaming.
- Proven experience training or fine-tuning custom LLMs, not only consuming managed APIs.
- Expertise with vector databases and embeddings, such as FAISS, Pinecone, and pgvector; feature stores; and modern data platforms such as Delta Lake or Lakehouse.
- Hands-on experience with multi-turn dialogue systems, conversational state handling, and intent orchestration at scale.
- Exposure to cloud AI services, including Azure OpenAI, Azure AI Search, AWS Bedrock, SageMaker, and Google Vertex AI; experience with containerization and Kubernetes.
- Knowledge of LLMOps and MLOps practices, including MLflow or Kubeflow, CI/CD, model registries, deployment strategies, monitoring for drift and toxicity, and observability.
- Strong understanding of enterprise integration patterns, including SOA, ESB, EAI, ETL, API-first design, event streaming, and microservices.
- Knowledge of security and governance, including Responsible AI, data privacy (PII and PCI), model risk management, access controls, secrets management, and threat modeling.
- Experience documenting conceptual, logical, and physical architectures; capability and process inventories; and clear architectural blueprints.
- Preferred certifications include TOGAF, Azure/AWS/GCP Cloud Architect, or Responsible AI certifications; exposure to Agile, Scrum, and DevSecOps is beneficial.
More Info
About Company
Skills:
networking), Enterprise integration approaches (SSO, Data Governance, Data Integration, Python, AI Architecture, Generative AI, Identity security and privacy, Vector databases, LLM APIs, API design patterns, Evaluation frameworks, RAG architectures
Skills:
data engineering , performing , Enterprise Architecture, Data Pipeline, Scalability, Enterprise Integration, LLMs, Embedding, performance metrics, Tool implementation, Ai, AI Governance, Accuracy and Precision, Software, experimentation, Azure AI
Skills:
banking solutions , Technical Leadership, Java 8, Agile, API Security, Computer Engineering, Spring Framework, Hardware delivery, AI Agents, Responsible AI and Generative AI Practices in Financial Services, Platform Modernization, Digital Banking, Demonstrate resilience, API Specification, Industry Stakeholders, Pre-Sales Support
Skills:
Sql, MLops, Cloud Architecture, Docker, Kubernetes, Python, Azure DevOps, Generative AI, enterprise data architecture, LLMOps, GitHub Actions, RAG, AI-ready data platforms, AI agents, AI security and governance
Skills:
Model Context Protocol (MCP), Retrieval-Augmented Generation (RAG), Agent-to-Agent (A2A) protocol, Large Language Models (LLMs), Kubernetes, Rust, Tensorflow, Typescript, Pytorch, scikit-learn, Agentic AI, CI/CD, Microsoft Agent Framework, Google Agent Development Kit