Job Title

GenAI Technical ArchitectExperience Level: - 10+ years overall IT experience, with 3+ years in AI/GenAI/LLM based solution architecture.Should have strong technical expertise in Python, hands-on experience with at least one GenAI framework (LangGraph, LangChain, or Google AI Development Kit), and strong working knowledge of one hyperscaler platform (Google Cloud, Azure, or AWS).The associate should lead solution design, integrating LLMs into enterprise workflows, mentoring team members, and driving production-grade implementation of GenAI use cases.Good knowledge of MLOps or DevOps to automate model deployment, versioning, and monitoring.Key Responsibilities:Architecture & Design.Design modular, scalable GenAI architectures leveraging LLMs, RAG, LangChain, LangGraph, Google ADK or Cortex Agents.Define architecture patterns for multi-agent systems, context-aware pipelines, and hybrid reasoning flows.Integrate LLMs (e.g., Llama, Gemini, GPT, Claude) into enterprise systems and custom applications.Develop reusable prompt orchestration and workflow frameworks.Establish standards for vector database (e.g., ChromaDB, Pinecone, FAISS, Weaviate, Vertex AI Matching Engine) usage, embeddings, and context retrieval.Architect scalable and secure GenAI microservices leveraging cloud-native components.Development & Implementation.Lead Python-based development efforts for building prompt orchestration, tool agents, and data pipelines.Develop and deploy APIs or microservices integrating LLMs with enterprise data sources.Design and deploy LLM-based microservices with robust error handling, observability, and scalability.Integrate custom models, open-weight models (e.g., Llama, Mistral), and API-based models (e.g., GPT, Claude, Gemini).Lead the end-to-end RAG lifecycle — ingestion, embedding, retrieval, generation, and evaluation.Implement prompt optimization, context management, and model performance tuning.Cloud Integration.Architect, deploy and monitor GenAI workloads on one hyperscaler:GCP (Vertex AI, Document AI, AlloyDB, BigQuery, Cloud Run)Azure (OpenAI Service, Cognitive Search, Azure ML)AWS (Bedrock, SageMaker, Lambda, API Gateway)Manage cloud infrastructure for scaling AI models, ensuring cost efficiency and compliance.Collaboration & Leadership.Lead a small team of AI engineers and developers.Partner with product and data teams to identify AI-driven business opportunities.Conduct code reviews, enforce best practices, mentor development teams on AI/ML implementation best practices.Review and optimize system designs for cost efficiency and latency performance.Contribute to governance, model safety, and compliance frameworks.Collaborate closely with product owners, data engineers, and business stakeholders to translate business needs into technical requirements.Contribute to internal GenAI capability building and reusable assets for the organization.Research & Innovation.Stay updated on LLM research, agentic frameworks, and GenAI trends.Protopye and evaluate multi-agent architectures, prompt optimization, and LLMOps pipelines.Experiment with prompt engineering, fine-tuning, and model evaluation metrics.Required Skills & Experience:Core Technical Skills.Python (advanced proficiency; ability to build APIs, pipelines, and modular frameworks).Hands-on experience with RAG systems, vector databases (FAISS, Pinecone, Chroma, Weaviate, or Snowflake Cortex Search).Hands-on with at least one GenAI framework:LangChain, LangGraph, or Google ADK (AI Development Kit).Solid understanding of LLMs (OpenAI, Anthropic, Meta, Mistral, Gemini, etc.) and token optimization strategiesExperience designing multi-agent or autonomous AI workflows.Expertise with LLM integration (OpenAI API, Gemini API, Ollama, Hugging Face, etc.).Experience with RAG, embeddings, and vector databases.Familiarity with PEFT, LoRA, or prompt fine-tuning approaches.Experience designing scalable microservices and event-driven architectures.Proven experience in production deployment, load balancing, and monitoring AI workloads.Knowledge of data engineering concepts — pipelines, ingestion, metadata, and data APIs.Familiarity with front-end integration (Streamlit, Gradio, or custom dashboards).Cloud / Hyperscaler Expertise (at least one required)Google Cloud Platform (GCP) – Vertex AI, Document AI, BigQuery, AlloyDB, Cloud Run, IAMAzure – Azure OpenAI, Cognitive Search, Azure ML, Azure FunctionsAWS – Bedrock, SageMaker, Lambda, API Gateway, DynamoDBSoft skills.Strong analytical and problem-solving mindset.Excellent communication and stakeholder management skills.Proven ability to lead technical discussions and drive cross-functional alignment.Other Desirable Skills.Knowledge of REST APIs, JSON, and FastAPI/Flask frameworks.Familiarity with data governance, PII handling, and AI ethics principles.Understanding of Docker/Kubernetes, CI/CD, and Git-based version control.Exposure to front-end integration with AI chat agents (React, Streamlit, Gradio, etc.) is a plus.Offshore, open to all TCS ODC located areas