Solutions Architect

4 days ago

hyderabad, India Tata Consultancy Services Full time

Greetings from TCS TCS is hiring for Solution architect -AI/genai/ML Role Solution architect -AI/GenAI/ML (Azure/AWS/Google) Required Technical Skill Set Expertise in designing GenAI architectures including LLM selection, RAG pipelines, vector databases, and integration patterns. GenAI frameworks and tools: LangChain, LlamaIndex, Haystack, Hugging Face Transformers, etc. Familiarity with prompt engineering, fine-tuning, RLHF, and MLOps workflows. Desired Experience Range Bachelor’s or Master’s in Computer Science, AI/ML, Engineering, or related field. 8+ years of experience in solution architecture or AI/ML roles, with 1–2 years focused on LLM or GenAI applications. Desired Competencies (Technical/Behavioral Competency) Must-Have Experience architecting AI solutions on at least one cloud platform: Azure: Azure OpenAI, Azure ML, Cognitive Services, Synapse AWS: Bedrock, SageMaker, Comprehend, Textract, Kendra GCP: Vertex AI, PaLM API, LangChain + BigQuery + Looker Hands-on with GenAI frameworks and tools: LangChain, LlamaIndex, Haystack, Hugging Face Transformers, etc. Familiarity with prompt engineering, fine-tuning, RLHF, and MLOps workflows. Knowledge of cloud-native architecture, REST APIs, containers (Docker, Kubernetes), and CI/CD. Experience with data privacy, model safety, bias mitigation, and AI governance principles. Good-to-Have Cloud certifications. Experience integrating GenAI into enterprise application Understanding of vector DBs (e.g., Pinecone, Weaviate, Chroma, Qdrant) and embedding models. Familiarity with Guardrails for LLMs, model monitoring, and LLMOps platforms. Responsibility of / Expectations from the Role Architect end-to-end Generative AI solutions for enterprise use cases such as: Agentic solutions, Chatbots and copilots, Knowledge assistants (e.g., RAG), Document summarization, generation, translation, Vision, speech, or code generation models Lead the design and integration of LLM pipelines with cloud-native services (e.g., serverless, containers, APIs). Select and fine-tune foundation models (OpenAI, Claude, Mistral, LLaMA, PaLM, etc.) as needed. Implement retrieval-augmented generation (RAG) using vector databases and hybrid search (e.g., FAISS, Pinecone, Weaviate). Design architectures that ensure scalability, security, and governance for GenAI applications. Build reference implementations, proof of concepts (PoCs), and reusable solution templates. Collaborate with data engineers, MLOps engineers, UI/UX designers, and product teams. Stay current with emerging GenAI trends, tools, models, and patterns.

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