Job Description: AI Engineer (Specialized AI & Agentic Systems) Role Overview: We are looking for a specialized AI Engineer to join our advanced intelligence division.

Unlike a traditional Full-Stack or Software Engineer, this role is strictly focused on the core AI lifecycle: from architecting sophisticated reasoning agents to deploying production-ready generative AI systems.

The successful candidate will focus exclusively on the mathematical, architectural, and operational aspects of Artificial Intelligence.

You will be responsible for building the "brains" of our systems-designing autonomous agentic workflows, high-precision retrieval systems, and large-scale recommendation engines-and ensuring they are robustly managed through advanced LLMOps and Agent

Ops frameworks.

Key Responsibilities Agentic AI Architecture: Design and implement sophisticated Multi-Agent Systems (MAS) and Agentic AI workflows capable of autonomous reasoning, tool-calling, and complex decision-making.

Advanced Retrieval & Search: Architect and optimize RAG (Retrieval-Augmented Generation) pipelines, high-performance AI Search engines, and advanced semantic indexing strategies.

Intelligence Systems: Develop and fine-tune Document Intelligence solutions, high accuracy Classification Systems, and personalized Recommendation Systems using both traditional ML and Generative AI techniques.

Generative AI Development: Build and optimize the core logic for GenAI applications, focusing on prompt engineering, model orchestration, and context window management. LLMOps & Agent

Ops:



Establish and maintain rigorous operational frameworks for the entire AI lifecycle, including model deployment, monitoring of non-deterministic outputs, agent performance tracking, and automated evaluation (LLM-as-a-judge).

Model Optimization: Evaluate and implement the most effective models (LLMs and SLMs) for specific use cases, balancing latency, cost, and reasoning capability.

Scalable Data Intelligence: Leverage enterprise-scale data platforms to build training and inference pipelines that power intelligent features.

Technical Qualifications 1.

Core AI: Generative AI: Deep expertise in LLM/SLM orchestration, advanced prompting techniques, and fine-tuning strategies.

Agentic Frameworks: Practical experience building with agentic frameworks (e.g., Lang

Graph, CrewAI, Auto

Gen, or custom-built orchestration layers).

Information Retrieval: Expertise in Vector Databases (e.g., Milvus, Pinecone, Weaviate), semantic search, and advanced RAG patterns (e.g., GraphRAG, HyDE).

Classical AI/ML: Solid foundation in supervised/unsupervised learning for classification, clustering, and recommendation engines. 2. AI Operations (LLMOps/Agent

Ops): Lifecycle Management: Experience in deploying, monitoring, and versioning AI models in production.

Observability:



Proficiency in implementing monitoring for AI agents (tracing agentic thought processes, tool-use success rates, and latency).

Evaluation: Ability to design robust evaluation frameworks to measure the accuracy, safety, and reliability of generative outputs. 3.

Infrastructure & Tool Stack: Databricks: Advanced proficiency in using Databricks for data engineering, machine learning (MLflow), and large-scale model training/serving. AWS: Extensive experience with AWS AI/ML services (e.g., Sage

Maker, Bedrock) and cloud infrastructure for scaling AI workloads.

Programming: Expert-level proficiency in Python for AI/ML development and scientific computing.

Preferred Qualifications Experience with the Model Context Protocol (MCP) for connecting agents to external data and tools. Deep understanding of transformer architectures and the mathematical principles behind attention mechanisms. Experience working within highly regulated, large-scale financial environments (e.g., implementing AI guardrails and security). Ability to transition research-grade AI concepts into production-grade, reliable intelligent systems.

Requirements Role Focus: This is a pure AI Engineering role.

Candidates should be prepared to work on algorithms, models, and orchestration layers rather than UI/UX or standard application CRUD operations.

Education: Advanced degree (Bachelor/MS/PhD) in Computer Science, Engineering, Mathematics, Statistics , or a related field is preferred, or equivalent deep industry experience in AI and engineering.

📌 Deloitte- AI Engineer (Specialized AI & Agentic Systems) - (India)
🏢 TRIGENT SOFTWARE PRIVATE
📍 India