Description

Key Responsibilities

Lead the architecture, development, and deployment of Agentic AI systems capable of autonomous reasoning, planning, decision-making, and execution.

Design and implement scalable AI solutions integrating SAP Joule with enterprise applications and business workflows.

Develop and orchestrate single-agent and multi-agent AI systems using frameworks such as LangChain, AutoGen, CrewAI, or equivalent technologies.

Design robust LLM orchestration, tool-calling, agent memory, workflow execution, and agent-to-agent communication patterns.

Build and optimize RAG pipelines using appropriate embedding models, retrieval strategies, reranking, and vector databases.

Integrate LLMs from providers such as OpenAI, Anthropic, and open-source models into enterprise AI applications.

Translate complex business requirements into scalable and production-ready AI/ML solutions.

Establish best practices for prompt engineering, model evaluation, AI observability, guardrails, and responsible AI.

Lead the deployment and optimization of AI applications across AWS, Azure, or GCP environments.

Work closely with product, engineering, SAP, and business stakeholders to identify and deliver AI-driven automation opportunities.

Mentor and provide technical guidance to AI engineers and developers.

Review architecture, code, performance, scalability, and security of AI solutions.

Ensure AI systems meet enterprise requirements for security, compliance, reliability, scalability, and data privacy.

Monitor production AI systems and optimize latency, cost, model performance, and reliability.

Mandatory Technical Skills

7–10+ years of experience in AI/ML Engineering, Generative AI, Data Science, or a closely related field.

Hands-on SAP Joule AI experience – Mandatory.

Strong practical experience in Agentic AI architecture and development.

Experience with one or more Agentic AI frameworks:

LangChain

LangGraph

AutoGen

CrewAI

Semantic Kernel

Equivalent agent orchestration frameworks

Strong programming skills in Python.

Hands-on experience with LLMs / Generative AI, including OpenAI, Anthropic, or open-source models.

Strong understanding of:

Prompt Engineering

RAG

Embeddings

Vector Databases

Tool Calling / Function Calling

AI Agents

Agent Memory

Multi-Agent Systems

LLM Orchestration

Experience with vector databases such as Pinecone, FAISS, Weaviate, Milvus, Chroma, or equivalent.

Experience deploying AI solutions on AWS, Azure, or GCP.

Strong understanding of AI application architecture, APIs, microservices, and integration patterns.

Experience with production deployment, monitoring, performance optimization, and troubleshooting of AI systems.

Strong technical leadership and stakeholder management capabilities.

SAP / Enterprise AI Experience

Candidates with experience in the SAP ecosystem will be preferred, particularly:

SAP Joule

SAP S/4HANA

SAP BTP

SAP Business AI

SAP Integration Suite

SAP enterprise workflows

SAP APIs / enterprise integrations

Enterprise automation

Good to Have

Experience leading AI transformation or Generative AI initiatives.

Experience implementing enterprise AI governance, security, and compliance.

Knowledge of AI evaluation frameworks and LLM observability platforms.

Experience with CI/CD and MLOps/LLMOps practices.

Experience integrating AI agents with enterprise applications and workflow platforms.

Exposure to MCP (Model Context Protocol) and modern AI tool-integration patterns.

Experience managing or mentoring a team of AI/ML engineers.

Strong understanding of enterprise architecture and solution design.

Leadership Expectations

Key Skills

Education

Any Gradute