Data Architect

Project Role : Data Architect

Project Role Description : Define the data requirements and structure for the application. Model and design the application data structure, storage and integration.

Must have skills : AI Agents & Workflow Integration

Good to have skills : NA

Minimum 7.5 Year(s) Of Experience Is Required

Educational Qualification : 15 years full time education

Role Overview :

As an AI Engineer (Agentic/Applied), you will design, build, and deploy production-grade agentic AI systems across the full enterprise technology stack. You will work directly with client engineering teams, lead technical design sessions, and build reusable patterns and accelerators that scale beyond individual engagements.

Roles & Responsibilities:

Design and build production-grade agentic systems end-to-end: multi-agent orchestration, RAG pipelines, policy-based routing, tool invocation, memory management, and lifecycle observability

Build and own RAG pipelines: embeddings, chunking strategy, vector search, context window engineering and tuning against real quality targets

Integrate and abstract across multiple LLM providers — OpenAI, Anthropic, Vertex AI, and open-source models — with fallback routing, token, cost, and latency management

Implement LLMOps in production: eval harnesses with real quality metrics, prompt versioning, observability tooling (LangSmith, Braintrust, or equivalent), cost and safety monitoring

Embed directly with client engineering teams to design, prototype, and deploy agentic solutions — workshops, proofs of concept, code-with sessions, and architecture walkthroughs

Build reusable patterns, accelerators, and playbooks that scale beyond the individual client engagement and enable the next one to start faster

Define and use metrics to measure agent accuracy, latency, safety, and cost-effectiveness present findings and recommendations to client stakeholders in business terms.

Professional & Technical Skills:

Software engineering experience in production environments

hands-on experience designing and deploying agentic AI solutions in a production environment — non-negotiable

Demonstrated experience with agentic orchestration frameworks: LangGraph, CrewAI, AutoGen, or equivalent — at production depth, not tutorial level

Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code: provider abstraction, token management, latency and cost tradeoffs

RAG pipeline ownership: embeddings, chunking strategy, vector databases, and context engineering

LLMOps fundamentals: eval harness design, prompt versioning, and production observability

Cloud-native engineering maturity: Kubernetes, Docker, microservices, serverless, CI/CD, and IaC (Terraform or Helm)

Strong Python Java or equivalent backend language acceptable production debugging and observability experience

Quality of experience is weighted over years, a candidate who has shipped three production agentic systems in four years is preferred over a generalist with passive AI exposure.

Additional Information:

• The candidate should have minimum 7.5 years of experience in AI Agents & Workflow Integration.
• This position is based at our Bengaluru office.
• A 15 years full time education is required.
  
  

Production agentic system shipped, at least one multi-step agentic system in a real environment. Design decisions must be articulable under questioning

RAG pipeline ownership: chunking decisions and metric-backed quality tradeoffs explained not I used LangChain

Multi-LLM provider integration in production: abstraction layer, fallback routing, cost and latency management across at least two providers

Eval harness built and defended: ran an evaluation framework with specific metrics, can defend every number and explain why each was chosen

LLMOps in production: prompt versioning, observability tooling, safety monitoring — active use, not awareness

Cloud-native maturity: Kubernetes, Docker, serverless, IaC — evidence of delivery ownership.