Deputy Director

Dear Candidate

We are looking for Deputy Director - GenAI role at Hyderabad location

Overview:

The AI Solution Engineer is a delivery focused engineer who designs, builds, and operates production agentic AI applications, along with the platform services those applications run on. Working within the technical direction set by AI Solutions and Platforms leadership, the AI Solution Engineer translates solution architecture into production grade agents, retrieval pipelines, tool and system integrations, evaluation harnesses, and the guardrails and observability that keep those systems reliable once live.

The AI Solution Engineer is a hands on practitioner who codes, integrates, tests, and iterates, owning feature level delivery across one or more solution or platform tracks while building a working understanding of the wider AI platform. The role works daily with modern AI engineering tooling, including agentic frameworks such as LangChain and LangGraph, the Model Context Protocol, and coding agents such as Claude Code, GitHub Copilot and Codex. This is a technical builder first, with a growing architect mindset

Qualifications:

• Bachelors degree in computer science, Engineering, Data Science or a related quantitative field. Masters preferred.
• 10 to 14 years in software, data or machine learning engineering, including at least 2 to 3 years building and running LLM and generative AI solutions in production.
• 2 or more years leading engineers, formally or as a senior technical lead, with evidence of having developed people.
• Strong hands-on Python. Code quality will be assessed during the interview process.
• Deep practical experience with agentic AI, covering tool calling, planning, memory, multi agent and agent to agent patterns, retrieval augmented generation, structured output, guardrails and evaluation.
• Production experience with LangChain and LangGraph, or an equivalent framework such as LlamaIndex, Semantic Kernel, CrewAI, Pydantic AI or custom orchestration, with a clear view of the tradeoffs.
• Daily working use of AI coding agents such as Claude Code, GitHub Copilot, Codex or Cursor, including agent configuration and custom tooling rather than inline completion alone.
• Solid understanding of LLM fundamentals, including context windows, embeddings, tokenization, fine tuning versus adapters versus prompting, and failure modes such as hallucination and prompt injection.
• Cloud and platform engineering experience. Azure preferred, covering Azure OpenAI or AI Foundry, AKS and Functions, or the equivalent on AWS or GCP. Docker, Kubernetes, Terraform or Bicep, CI/CD and API design.
• Data foundations, including SQL, Spark or Databricks, vector stores, and real experience working with messy unstructured technical documents.

Interested candidates can share their updated resumes at [email protected]