AI​/ML Engineer

At Siemens Energy, we can. Our technology is key, but our people make the difference. Brilliant minds innovate. They connect, create, and keep us on track towards changing the world's energy systems. Their spirit fuels our mission.

Our culture is defined by caring, agile, respectful, and accountable individuals. We value excellence of any kind. Sounds like you

Role: AI & ML Engineer (3–5 years)

Location:

Gurugram (Preferred), Bengaluru, Pune

Employment Type:

Full-time

About

The Role

We're seeking a hands-on AI/ML Engineer to design, build, and product ionize 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, expertise in AI foundation models, and the ability to translate cutting-edge concepts into impactful production grade solutions for Digital Grid challenges.

Key Responsibilities

End-to-End GenAI Development:
Design and implement RAG pipelines, agentic workflows, and LLM integrations for tasks such as document understanding, generation, and knowledge assistance.

Multi-Agent Orchestration:
Build agent-based applications for planning, tool use, and execution using frameworks like Lang Graph, Semantic Kernel, and prompt orchestration tools.

Dev Ops/MlOps:
Build production grade scalable, resilient cloud-based apps for deployment, and monitoring on Azure/AWS with Docker, Kubernetes, Containers and CI/CD.

AI Enterprise Architecture:
Understanding of AI architecture (scalable, modern, and secure) design across AI/ML enterprise solutions.

Rapid Prototyping to Production:
Convert problem statements into prototypes, iterate with stakeholders, and harden into production-ready microservices (FastAPI) with APIs and event-driven workflows.

Evaluation & Reliability:
Define rigorous evaluation metrics for LLM/ML systems (accuracy, latency, cost, safety), optimize retrieval quality, prompt strategies, and agent policies.

Security & Compliance:
Implement Responsible AI guardrails, data privacy, PII handling, access controls, and auditability.

Collaboration & Enablement:
Partner with data engineers, mentor junior team members, and contribute to internal documentation and demos.

What You'll Bring

Education:

Bachelor's/Master's in Computer Science, Data Science, Engineering, or equivalent experience.

Experience:

3–5 years delivering AI/ML, Data Science solutions in production.

2-3 years focused on Generative AI/LLM applications.

Technical

Skills:

Programming:
Strong Python (typing, packaging, testing), data stacks (Num Py, 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).

Agentic frameworks (Lang Graph, Semantic Kernel, MAF) and orchestration strategies.

Model selection/fine-tuning, cost-performance optimization, Guardrails

Cloud & Data:
Hands-on with Azure/AWS; experience with Azure OpenAI, Azure AI Search, Microsoft Fabric/Databricks , Snowflake or similar DWH.

MLOps/Dev Ops:
Docker, Containerization, Queuing, Load Balancing, handling Concurrency, Kubernetes, CI/CD (Git Hub /Gitlab), Deployment/Monitoring.

Architecture:
Understanding of Microservices, event-driven design, API security, scalability, and resilience.

Soft Skills:

Excellent team player with the ability to work collaboratively in cross-functional and multicultural teams.

Strong communication skills able to explain complex technical ideas to non-technical stakeholders.

Adaptability to changing priorities and evolving technologies.

Problem-solving mindset with creativity, curiosity, and a proactive approach.

Strong sense of ownership and accountability over deliverables.

Domain Knowledge:
Experience applying AI/ML to power systems, electrical grids, or related domains.

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 (Open Telemetry), and cost governance (token budgeting).

Tech Stack

Languages/Frameworks:
Python, FastAPI/Flask, Lang Graph/Semantic Kernel/CrewAI/Auto Gen, scikit-learn, PyTorch/Tensor Flow.

LLM & Retrieval:
Azure OpenAI/Open weights, embeddings, vector DBs (FAISS/Milvus/Pinecone), reranking.

Data & Cloud:
Snowflake, Azure/AWS (storage, compute, messaging), SQL.

MLOps:
Docker, Kubernetes, Azure Functions, Azure Container Apps, Azure Service Bus, Azure API Management, Gitlab

Collaboration:

Git, Jira/Azure Dev Ops, Agile/Scrum.