Senior AI Solution Engineer _ GSO

Siemens; Maharashtra; On-site; INR 1,800,000 - 2,600,000; Full time

Job summary

Siemens seeks a Senior AI Solution Engineer to drive AI adoption across service operations, prototyping practical AI with measurable business value. The role focuses on Generative AI, RAG pipelines, and multi-agent systems to automate workflows and boost operational excellence.

You’ll collaborate with architects, data engineers, and domain experts to rapidly prototype, deliver scalable microservices (FastAPI), and govern responsible AI practices.

Qualifications

• Bachelor’s/Master’s in computer science, Data Science, Engineering, or equivalent experience.

Responsibilities

• Identify and assess AI opportunities across service operation units.
• Conduct workshops and convert challenges into AI use cases.
• Rapidly design and develop proofs-of-concept using Azure AI Foundry, Copilot, Power Platform, Mendix.
• Support deployment, monitoring, and continuous improvement of AI solutions.
• Provide technical leadership on AI governance and best practices.
• Track emerging technologies and recommend innovations.

Skills

Python

LLM/GenAI

MLOps

Cloud Azure

Data Pipelines

API Development

LangGraph

Semantic Kernel

Education

Bachelor's / Master's in CS or related

4+ years AI/ML prod experience

2-3 years Generative AI/LLM

Tools

FAISS

Pinecone

Azure OpenAI

Docker

Kubernetes

FastAPI

Job description

We’re seeking a Senior AI Solution engineer to drive AI adoption across one of the service operation business units. Major responsibility will be to identify, prototype and implement practical AI that delivers measurable business value.

Senior AI solution engineer will be responsible for producing Generative AI solutions, including RAG pipelines and multi‑agent systems—to automate workflows and drive operational excellence. You’ll work closely with solution/data architects, software developers, data engineers, and domain experts to rapidly prototype and deliver scalable, enterprise‑grade systems.

This is an individual contributor role requiring strong research skills, deep expertise in AI foundation models, and the ability to translate cutting‑edge concepts into impactful solutions for service operation challenges.

A Snapshot of your Day

How You’ll Make An Impact (responsibilities Of Role)

Strategic

• Identify and assess AI, GenAI, Agentic AI, and automation opportunities across service operation business units.
• Conduct business workshops and convert operational challenges into AI‑enabled use cases focused on automation and productivity enhancement opportunities.
• Rapidly design and develop proof‑of‑concepts using Azure AI Foundry, Microsoft Copilot, Power Platform, and Mendix.
• Support deployment, monitoring, and continuous improvement of AI solutions.
• Provide technical leadership on AI governance, responsible AI, and adoption of best practices.
• Track emerging technologies and recommend innovations that create business value.

Operational

• Design and implement RAG pipelines, agentic workflows, and LLM integrations for tasks such as document understanding, classification, and knowledge assistance.
• Build agent‑based applications for planning, tool use, and execution using frameworks like LangGraph, Semantic Kernel, and prompt orchestration tools.
• Architect data pipelines and cloud solutions for training, deployment, and monitoring on Azure/AWS with Docker, Kubernetes, and CI/CD.
• Convert problem statements into prototypes, iterate with stakeholders, and harden into production‑ready microservices (FastAPI) with APIs and event‑driven workflows.
• Define rigorous evaluation metrics for LLM/ML systems (accuracy, latency, cost, safety), optimize retrieval quality, prompt strategies, and agent policies.
• Implement Responsible AI guardrails, data privacy, PII handling, access controls, and auditability.

What You Bring (required Qualification And Skill Sets)

• Bachelor’s/master’s in computer science, Data Science, Engineering, or equivalent experience
• This role requires 4+ years of experience and ownership of domain‑specific strategy, overnance, delivery and stakeholder engagement.
• 4–6 years delivering AI/ML, Data Science solutions in production.
• 2-3 years focused on Generative AI/LLM applications.
• Experience delivering enterprise‑scale digital solutions
• Strong communication and stakeholder management skills
• Experience working in global cross‑functional teams
• Strong Experience in AI architecture (scalable, modern, and secure) design across AI/ML enterprise solutions.
• Technical Skills:
• Programming: Strong Python (typing, packaging, testing), data stacks (NumPy, Pandas, scikit‑learn), API development (FastAPI/Flask).
• GenAI Expertise:
• Prompt engineering, RAG design (indexing, chunking, reranking).
• Embeddings and vector databases (FAISS, Azure AI Search, Pinecone).
• Agent frameworks (LangGraph, Semantic Kernel) and orchestration strategies.
• Model selection/fine‑tuning, cost‑performance optimization, safety filters.
• Cloud & Data: Hands‑on with Azure/AWS; experience with Azure OpenAI, Azure AI Search, Microsoft Fabric/Databricks (preferred), Snowflake or similar DWH.
• MLOps: Docker, Kubernetes, CI/CD (GitHub Actions/Gitlab), model deployment / monitoring.
• Architecture: Microservices, event‑driven design, API security, scalability, and resilience.

Preferred Qualifications

• Experience with Azure OpenAI, Microsoft Fabric/Prompt Flow, Copilot Studio connectors, or enterprise integrations (SharePoint/Teams).
• Expertise in ML/DL techniques: time‑series forecasting, anomaly detection, NLP document AI (OCR, classification, extraction).
• Familiarity with security (OAuth2, RBAC), observability (OpenTelemetry), and cost governance (token budgeting).

Tech Stack

• Languages/Frameworks: Python, FastAPI/Flask, LangGraph/Semantic Kernel/CrewAI/AutoGen, scikit‑learn, PyTorch/TensorFlow.
• LLM & Retrieval: Azure OpenAI/Open weights, embeddings, vector DBs FAISS/Milvus/Pinecone), reranking.
• Data & Cloud: Snowflake, Azure/AWS (storage, compute, messaging), SQL.
• Ops: Docker, Kubernetes, GitHub Actions/Jenkins, Helm, monitoring/logging.
• Collaboration: Git, Jira/Azure DevOps, Agile/Scrum.