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Senior AI & ML Engineer GA

Job ID
491932
Veröffentlicht seit
29-Jan-2026
Organisation
Siemens Energy
Tätigkeitsbereich
Product Management, Portfolio & Innovation
Unternehmen
SIEMENS ENERGY INDIA LIMITED
Erfahrungsniveau
Experienced Professional
Beschäftigungsart
Vollzeit
Arbeitsmodell
Arbeiten vor Ort
Vertragsart
Unbefristet
Standort(e)
  • Gurugram - Haryana - Indien
Title: Senior AI & ML Engineer GA

Position Summary:
We’re seeking a hands-on AI/ML engineer to design, build, and productionize 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 digital grid challenges.

How You’ll Make an Impact (responsibilities of role)

  • End-to-End GenAI Development: Design and implement RAG pipelines, agentic workflows, and LLM integrations for tasks such as document understanding, classification, and knowledge assistance.
  • Multi-Agent Orchestration: Build agent-based applications for planning, tool use, and execution using frameworks like LangGraph, Semantic Kernel, and prompt orchestration tools.
  • AI Enterprise Architecture: Strong Experience in AI architecture (scalable, modern, and secure) design across AI/ML enterprise solutions.
  • Data & MLOps Foundations: Architect data pipelines and cloud solutions for training, deployment, and monitoring on Azure/AWS with Docker, Kubernetes, and CI/CD.
  • 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 Bring (required qualification and skill sets)

  • Bachelor’s/master’s in computer science, Data Science, Engineering, or equivalent experience with 7–12 years delivering AI/ML, Data Science solutions in production and 2-3 years focused on Generative AI/LLM applications.
  • 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.
  • Ability to translate business needs into technical solutions, communicate effectively with non-technical stakeholders, and work independently on rapid iterations.
  • 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 (OpenTelemetry), and cost governance (token budgeting).
  • Grid Automation-Relevant Skills (Good to Have):
  • Understanding of electrical engineering fundamentals, including the ability to read and interpret Single Line Diagrams (SLDs) – familiarity with standard electrical symbols (e.g., transformers, breakers, relays), power flow tracing, and substation configurations.
  • Experience with computer vision or multimodal AI for processing engineering drawings (e.g., symbol detection, layout analysis, and automated digitization of SLDs or schematics using OCR, object detection models like YOLO, or vision transformers).
  • Knowledge of advanced document AI techniques for technical diagrams, such as extraction of components, connections, and metadata from scanned/grid drawings to enable RAG-based querying or automation.
  • Exposure to AI applications in power systems, such as predictive maintenance for grid assets, fault detection in transmission/distribution, or optimization of grid operations using time-series ML models.

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.