Summary
Position Summary
Artificial Intelligence & Engineering
AI & Engineering leverages cutting-edge engineering capabilities to help build, deploy, and operate integrated/verticalized sector solutions in software, data, AI, network, and hybrid cloud infrastructure. These insights are powered by engineering for business advantage, helping transform mission-critical operations.Join our AI & Engineering team to help transform technology platforms, drive innovation, and help make a significant impact on our clients achievements. You'll work alongside talented professionals reimagining and re-engineering operations and processes that could be critical to businesses.
Position Summary
Level:
ConsultantAs an experienced Consultant at Deloitte Consulting, you will be responsible for individually delivering high quality work products within due timelines. Need-basis you will be mentoring and/or directing junior team members/liaising with onsite/offshore teams to understand the functional requirements.You will design, develop, and deploy intelligent agent systems that can autonomously reason, plan, and act to achieve complex objectives. You will work at the cutting edge of AI, combining advancements in LLMs, autonomous reasoning frameworks, reinforcement learning, and planning systems to build truly agentic software. You will collaborate closely with senior developers and architects, ensuring technical excellence, efficient delivery, and client satisfaction.
Work you'll do:
Apply industry knowledge to requirements gathering and analysis, ensuring alignment with sector needs. Demonstrate expertise across the entire software development lifecycle, from design and coding to deployment and defect resolution, collaborating with stakeholders for effective delivery. Conduct peer and team reviews to maintain quality standards. Actively engage in Agile practices, including sprint planning, retrospectives, and effort estimation. Build and share business process knowledge, supporting project knowledge management and team training. Proactively recommend process improvements, track efficiency gains, and contribute to automation and innovation, all aimed at enhancing project outcomes and delivering greater value to clients.
- Design and implement intelligent, autonomous agents capable of reasoning, planning, and taking goal-driven actions leveraging Retrieval-Augmented Generation (RAG) systems using LLMs, vector databases, and contextual embedding workflows
- Implement agent-reasoning workflows using state-of-the-art agent frameworks (e.g., LangChain , AutoGPT, Crewai, Langgraph, ReAct, Agno) for automation and intelligent task chaining.
- Build and optimize vector search systems using FAISS, Pinecone, Weaviate, Milvus, or Chroma for low-latency retrieval.
- Integrate embeddings (OpenAI, Hugging Face, Instructor models) into semantic search and contextual knowledge pipelines.
- Collaborate with data scientists, ML engineers, and DevOps teams to containerize and deploy Gen AI services via Docker/Kubernetes.
- Integrate APIs with enterprise systems using REST/gRPC for scalable and modular Gen AI applications.
- Contribute to LLM evaluation, performance tuning, and prompt optimization to improve contextual accuracy and efficiency.
- Participate actively in Agile practices sprint planning, reviews, retrospectives, and knowledge sharing across teams.
The team:
At Hybrid Cloud Infrastructure, we deliver solutions spanning Hybrid Cloud, Advanced Connectivity, AI Data Centers, High-Performance Computing, and AI Infrastructure to help clients achieve their desired outcomes. Our offerings include engineered transformation services for hybrid cloud infrastructure and platforms, prioritizing resiliency, optimization, and extensive automation. We integrate Advanced Connectivity, with AI Infrastructure and AI to boost operational efficiency and enable real-time data processing, crucial for critical low-latency enterprise operational technology (OT) applications. Additionally, we provide comprehensive management of all facets of operations for hybrid cloud infrastructure and field operations.
Qualifications
Must Have Skills/Project Experience/Certifications:
- 36 years of hands-on experience in AI/ML or Gen AI solution development including LLM integration and RAG systems.
- Proficiency in Python, Langchain, or Llama Index with exposure to embedding and vector search workflows.
- Experience with vector databases (FAISS, Pinecone, Milvus, Weaviate, or Chroma) for semantic retrieval.
- Understanding of LLMs (OpenAI GPT, Claude, Llama, Mistral, etc.) and embedding models for context-aware responses.
- Experience implementing agentic workflows or single/multi-agent task frameworks.
- Exposure to cloud platforms (Azure, AWS, or GCP) for AI/ML deployment and container orchestration (Docker/Kubernetes).
- Experience with REST/gRPC API integration, microservice architecture, and pipeline optimization.
- Strong analytical, collaboration, and communication skills across distributed teams.
Good to Have Skills/Project Experience/Certifications:
- Experience with enterprise search, document intelligence, or RAG-enhanced chatbots.
- Exposure to LangGraph, CrewAI, or AutoGen for agentic orchestration and workflow design.
- Knowledge of LLM fine-tuning, quantization, or distillation techniques for efficiency.
- Experience with OpenAI function calling, Anthropic Tools API, or custom agent integration.
- Understanding of Gen AI evaluation tools like PromptBench, TruLens, or RAGAS.
- Certifications in AI/ML, cloud computing, or vector databases.
Education:
- BE/B. Tech/M.C.A./M.Sc. (CS) degree or equivalent from accredited university
Location:
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Recruiting tips
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