AI Applications – DevOps Engineer

Position: AI Applications – DevOps Engineer (CE712SF RM 3625)

Overview

We are seeking an experienced AI Applications/ DevOps Engineer Engineer to architect, implement, and optimize advanced AI solutions, with a particular focus on Large Language Models (LLMs), agentic pipelines, workflow automation, and generative AI. You will work on high-impact initiatives across engineering automation, smart knowledge retrieval, and autonomous agent driven workflows using the latest in AI research and toolkits.

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, documment 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.

Essential Qualifications

Bachelor's/Master's/PhD in Computer Science, Artificial Intelligence, or related field.

Deep expertise in building with LLMs (commercial and open-source): prompt engineering, model selection, fine-tuning, and evaluation.

Hands-on experience developing agentic pipelines and workflow automations using frameworks like LangChain, LlamaIndex, Semantic Kernel, Haystack, and orchestration of cloud/on-prem LLM endpoints.

Proven track record designing RAG systems (vector database management, chunking strategies, search optimization, retrieval pipelines—using Pinecone, Weaviate, FAISS, ChromaDB, Elastic, etc.).

Working knowledge of multi-modal AI (text/audio/image/diagram/video handling), Graphbased retrieval, knowledge graphs, and semantic search.

Strong Python skills, deep experience with modern AI/ML/NLP libraries (Transformers, Pydantic, FastAPI, HuggingFace, Azure OpenAI, etc.).

Experience integrating AI solutions into real-world engineering or enterprise applications (APIs, plugins, workflow tools, agent frameworks, MLOps/LLMOps).

Familiarity with advanced prompting, guardrails/AI safety, evaluation and monitoring of AI systems, and leveraging synthetic data.

Preferred/Bonus:

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).

General:

Strong critical thinking and research skills, enthusiastic about rapid learning and experimenting with new AI capabilities.

Excellent communication and documentation abilities.

Ability to work in fast-moving, highly collaborative environments with evolving requirements.

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Job Category: Digital_Cloud_Web Technologies

Job Type: Full Time

Job Location: remote

Experience: 7-12 Years

Notice period: 0-15 days