Assistant Manager | AI Engineer | Bengaluru | Sustainability & Emerging Assurance

Deloitte & Touche GmbH Wirtschaftsprüfungsgesellschaft; Bengaluru; On-site; INR 900,000 - 1,500,000; Full time

Job summary

Deloitte & Touche GmbH Wirtschaftsprüfungsgesellschaft in Bengaluru invites an Assistant Manager | AI Engineer to design, develop, and scale AI solutions that automate business processes and enhance knowledge discovery. The role spans AI Engineering, software development, cloud engineering, and MLOps for production-grade outcomes.

The ideal candidate will drive deployment, monitoring, and optimization of AI workflows using LLMs, RAG, and vector databases in a collaborative, Agile setting.

Qualifications

• Proven experience developing enterprise-grade AI applications.
• Ability to work across AI engineering, software development, cloud engineering and platform operations.
• Experience with Agile environments and stakeholder management.

Responsibilities

• Design, develop, and scale Agentic AI solutions and AI assistants.
• Build and optimize RAG applications and enterprise knowledge systems.
• Develop knowledge ingestion, extraction, indexing, and retrieval pipelines.

Skills

Python programming

AI development

Cloud engineering

LLM workflows

Retrieval-Augmented Generation

MLOps

Education

Any Graduation: B.Tech,B.Com,B.Sc,MBA,M.Tech

Tools

LangGraph

Haystack

OpenAI SDK

LanceDB

FalkorDB

vLLM

SGLang

Langfuse

Ray

Azure

Job description

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Job Title: Assistant Manager | AI Engineer | Bengaluru | Sustainability & Emerging Assurance

TheTeam

Assurance is about much more than just the numbers.It’sabout attesting to accomplishments and challenges and helping to assure strong foundations for future aspirations. Deloitte exemplifieswhat, how, and why of change soyou’realways ready to act ahead. Learn more about Audit & Assurance Practice .

Design, develop, and scale Agentic AI solutions, AI Assistants, and Workflow Automation platforms powered by Large Language Models (LLMs). This role combines AI Engineering, Software Development, Cloud Engineering, and MLOps to deliver intelligent, production-grade solutions that automate business processes, enhance knowledge discovery, and drive operational efficiency. The ideal candidate is passionate about emerging AI technologies and capable of working across the full AI lifecycle from experimentation and development to deployment, monitoring, and optimization.

Key Skills Required

• Any Graduation : B.Tech,B.Com,B.Sc,MBA,M.Tech
• Design and develop AI assistants, autonomous agents, and agentic workflow automation solutions leveraging Large Language Models (LLMs).
• Build, deploy, and optimize Retrieval-Augmented Generation (RAG) applications and enterprise knowledge systems.
• Develop knowledge ingestion, extraction, indexing, and retrieval pipelines using structured and unstructured data sources, including documents, APIs, databases, and web content.
• Implement advanced retrieval mechanisms including chunking strategies, semantic search, vector search, hybrid search, and graph-based retrieval.
• Develop scalable AI services, APIs, and cloud-native applications supporting enterprise AI workloads.
• Deploy, monitor, and optimize containerized AI applications in cloud environments.
• Establish AI testing, benchmarking, evaluation, and experimentation frameworks to ensure model quality and reliability.
• Apply software engineering best practices including automated testing, version control, observability, CI/CD, and operational excellence.
• Collaborate with cross-functional teams to translate business requirements into scalable AI-powered solutions.
• Continuously evaluate emerging AI technologies, frameworks, and architectures to improve solution effectiveness and innovation.

Technical Skills

• Strong programming expertise in Python for AI application development, automation, and backend services.
• Hands-on experience developing AI, Machine Learning, and data-driven solutions.
• Experience building applications powered by Large Language Models (LLMs) and Generative AI technologies.
• Strong understanding of Agentic AI architectures, AI Assistants, and workflow orchestration concepts.
• Experience designing and implementing RAG solutions, embeddings, vector databases, semantic search, and chunking strategies.
• Knowledge of data ingestion, web scraping, data extraction, and unstructured data processing techniques.
• Experience with Git, software testing frameworks, and CI/CD pipelines.
• Strong understanding of cloud-native architectures and Microsoft Azure services.
• Familiarity with MLOps and LLMOps practices including deployment, monitoring, observability, and lifecycle management of AI applications.
• Experience performing AI model evaluation, benchmarking, testing, and performance optimization.
• Knowledge of API development, distributed systems, and scalable platform engineering.
• AI & Agent Frameworks: LangGraph, Haystack, OpenAI SDK, or similar frameworks.
• Knowledge & Retrieval Platforms: LanceDB, FalkorDB, vector databases, and retrieval systems.
• Model Serving & Inference: vLLM, SGLang, or comparable model-serving technologies.
• Development Technologies: Python, Ray, and modern software development frameworks.
• Observability & Evaluation: Langfuse or equivalent AI observability and monitoring platforms.

Professional Qualifications

• Proven experience developing and deploying enterprise-grade AI applications and intelligent automation solutions.
• Strong analytical, problem-solving, and debugging capabilities.
• Ability to work across multiple disciplines including AI Engineering, Software Development, Cloud Engineering, and Platform Operations.
• Experience working in Agile and collaborative development environments.
• Excellent communication and stakeholder management skills.
• Demonstrated ability to learn and adapt quickly in a fast-evolving AI ecosystem.

Preferred Qualifications

• Experience with workflow orchestration frameworks and advanced agentic design patterns.
• Knowledge of hybrid search, retrieval optimization, and advanced RAG architectures.
• Experience with knowledge graphs, graph databases, and graph-based retrieval solutions.
• Familiarity with fine-tuning techniques such as LoRA, QLoRA, PEFT, and adapter-based tuning methods.
• Experience serving and deploying models using vLLM, SGLang, or similar inference platforms.
• Knowledge of AI observability, monitoring, tracing, and performance analysis frameworks.
• Understanding of AI solution strategy, including when to leverage prompting, RAG, knowledge graphs, fine-tuning, or a combination of approaches to achieve business objectives.
• Track record of delivering measurable business value through AI-driven automation, intelligent assistants, and enterprise AI platforms.