Description :

As a Senior AI Engineer, you will play a key role in designing and delivering advanced AI solutions with a strong emphasis on Generative AI and Large Language Models (LLMs). You will apply scientific rigor to develop scalable, production-ready machine learning systems that drive measurable business impact, working on challenging problems in forecasting, demand planning, renewable energy optimization, anomaly detection, and prescriptive maintenance. With minimum 5 years of industry experience, you are expected to bring strong expertise in statistical modeling, ML engineering, and modern AI architectures, particularly in GenAI and LLM-based applications. This role offers the opportunity to work on high-impact projects that shape next-generation AI capabilities within the organization.

You Will :

- Design, develop, and deploy machine learning and Generative AI solutions to solve complex business problems

- Build, fine-tune, and optimize Large Language Models (LLMs) and transformer-based architectures for real-world applications

- Apply rigorous scientific methodologies to experimentation, model evaluation, and performance optimization

- Develop scalable ML pipelines and production-grade systems in collaboration with Engineering teams

- Conduct prompt engineering, model alignment, evaluation, and performance benchmarking for GenAI applications

- Work closely with Product, Engineering, and Business stakeholders to translate ambiguous requirements into data-driven AI solutions

- Instrument and monitor LLM applications in production using observability tools, tracking cost, latency, quality, and drift

- Contribute to model governance, responsible AI practices, and performance monitoring in production environments

- Stay current with advancements in Generative AI, LLM research, and applied machine learning, incorporating relevant innovations into company solutions

You'll Have :

- Masters or PhD in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field

- Minimum 5 years of hands-on industry experience in AI engineering, machine learning, data science, or applied AI roles

- Strong experience with Generative AI frameworks and Large Language Models (e.g., transformer architectures, fine-tuning, RAG systems)

- Proficiency in Python and modern ML/AI libraries such as PyTorch, TensorFlow, Hugging Face, or equivalent ecosystems

- Solid understanding of statistical modeling, experimentation, and model evaluation methodologies

- Experience building and deploying ML models into production environments

- Familiarity with data engineering workflows and cloud-based ML platforms (AWS, GCP, or Azure)

- Strong problem-solving skills with the ability to work independently on complex and ambiguous problem statements

- Excellent communication skills with the ability to present technical insights clearly to cross-functional stakeholders

Preferred Qualifications :

- Experience implementing Retrieval-Augmented Generation (RAG), vector databases, and embedding-based search systems

- Hands-on experience with LLM observability platforms (e.g., Langfuse, LangSmith, Arize Phoenix, Weights & Biases) for tracing, cost tracking, and quality monitoring in production

- Experience with LLM evaluation frameworks (e.g., RAGAS, DeepEval) and evaluation patterns such as LLM-as-judge and automated regression testing

- Practical experience deploying LLM applications with guardrails, prompt versioning, hallucination detection, and model drift monitoring

- Exposure to distributed training, model optimization, and scalable inference architectures

- Knowledge of MLOps practices, CI/CD for ML (Travis CI, Jenkins), and model lifecycle management

- Prior experience applying AI solutions in industrial or asset-intensive environments

- Experience working in fast-paced startup or product-driven environments