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 robust 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.
- 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.

- 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.

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.

📌 Senior Ai & Ml Engineer (Pune)
🏢 Siemens
📍 Pune