Senior AI/ML Engineer - NLP & Generative AI

Siemens

5 - 10 years

6 - 12 Lacs

Bengaluru Karnataka India

Posted:2 weeks ago| Platform: Foundit logo

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Skills Required

AI/ML Generative AI RAG pipelines

Work Mode

On-site

Job Type

Full Time

Job Description

We are looking for

You'll make an impact by

Design, develop, and optimize NLP-driven AI solutions using state-of-the-art models and
techniques (NER, embeddings, summarization, etc.).
Build and productionize RAG pipelines and agentic workflows to support intelligent, context aware applications.
Fine-tune, prompt-engineer, and deploy LLMs (OpenAI, Anthropic, Falcon, LLaMA, etc.) for domain-specific use cases.
Collaborate with data scientists, backend developers, and cloud architects to build scalable AI first systems.
Evaluate and integrate third-party models/APIs and open-source libraries for generative use cases.
Continuously monitor and improve model performance, latency, and accuracy in production settings.
Implement observability, performance monitoring, and explainability features in deployed models.
Ensure solutions meet enterprise-level requirements for reliability, traceability, and maintainability.

Use your skills to move the world forward!

Master's or Bachelor's degree in Computer Science, Machine Learning, AI, or a related field.
5+ years of overall experience in AI/ML, with at least 2+ years in NLP and 1'2 years in Generative AI.
Strong understanding of LLM architectures, fine-tuning methods (LoRA, PEFT), embeddings, and vector search.
Experience in designing and deploying RAG pipelines and working with multi-step agent
architectures.
Proficiency in Python and frameworks like Lang Chain, Transformers (Hugging Face), Llama Index, Smol Agents, etc.
Familiarity with ML observability and explainability tools (e.g., Tru Era, Arize, Why Labs).
Knowledge of cloud-based ML services like AWS Sagemaker, AWS Bedrock, Azure OpenAI Service, Azure ML Studio, and Azure AI Foundry.
Experience in integrating LLM-based agents in production environments.
Understanding of real-time NLP challenges (streaming, latency optimization, multi-turn dialogues).
Familiarity with Lang Graph, function calling, and tools for orchestration in agent-based systems.
Exposure to infrastructure-as-code (Terraform/CDK) and DevOps for AI pipelines.
Domain knowledge in Electrification, Energy, or Industrial AI is a strong plus.

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