We are looking forSenior Software Engineer (AI/ML NLP & Generative AI),
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.
