Gen AI Specialist

Job Description & Skill Requirement:

Core Responsibilities

• Design and build advanced AI-driven systems utilizing LLMs (e.g., Azure OpenAI GPT

Models, Claude, Llama, Mistral, Gemini, and open-source models) for tasks such as text

understanding, generation, summarization, and contextual reasoning within engineering

workflows.

• Architect and deploy agentic pipelines (multi-agent systems, autonomous LLM agents,

chain-of-thought/reasoning systems) for process automation, decision support, and

engineering knowledge orchestration.

• Develop and implement Advanced Retrieval-Augmented Generation (RAG) solutions —

combining LLMs with vector databases, search engines, and enterprise knowledge sources

for high-fidelity document analysis and Q&A.

• End-to-End automation of complex human-in-the-loop processes by chaining LLMs, expert

systems, and external tools using orchestration frameworks (such as LangChain,

LlamaIndex, Haystack, CrewAI, etc.).

• Evaluate, select, and integrate modern and emerging AI tools, APIs, and infrastructure

(LLMOps, vector stores, document loaders, prompt management, agents frameworks, etc).

• Fine-tune, deploy, and monitor LLMs on private/in-house datasets to solve unique domain

challenges and maintain compliance/privacy.

• Stay current with the fast-evolving AI landscape (open weights, small/efficient models,

guardrails, synthetic data, evaluation techniques, multimodal models, etc.), and bring new

approaches into the organization.

Preferred:

• Experience optimizing for model cost, latency, reliability, and scaling in production.

• Understanding of privacy, security, and compliance in LLM/AI applications (PII scrubbers,

access controls, audit trails).

• Experience orchestrating multi-agent/agentic workflows (CrewAI, AutoGen, OpenAgents,

etc.).

• Familiarity with CI/CD for AI pipelines, containerization (Docker), and cloud AI services

(Azure ML, AWS Sagemaker, GCP Vertex).