Solutions Architect

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.