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Senior AI & ML Engineer

Siemens -
Bengaluru, Karnataka

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Job details

Permanent | Full-time
3 hours ago

Qualifications

Jira
TensorFlow
CI/CD
Azure
Law
Computer Science
Kubernetes
NumPy
Git
Research
Enterprise architecture
Master's degree
SQL
Pandas
RBAC
OS Kernels
AWS
Electrical experience
Docker
Bachelor's degree
Continuous integration
Forecasting
Natural language processing
SharePoint
GitHub
APIs
Scalability
Agile
Data science
Flask
AI
Jenkins
GitLab
Communication skills
Python
Data Science

Full job description

We’re seeking a hands-on AI/ML engineer to design, build, and produce 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.

A Snapshot of your Day

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)

Education: Bachelor’s/master’s in computer science, Data Science, Engineering, or equivalent experience
Experience:
- 7–12 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 (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.
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.
- Time management & prioritization in fast-paced, iterative environments.
- A mentoring attitude toward junior colleagues and an openness to receiving feedback.
- 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 (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.

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