Job Description

Role: Senior AI Engineer
Experience: 4 to 6 years
Reporting To: Director of Engineering
Location: WFO, HSR Layout Bengaluru

About Astuto

We are a Cloud FinOps startup focused on helping businesses save Cloud costs, avoid waste, improve efficiency, and optimize their ROI on Cloud Spend.
Our vision is to build OneLens as one of the industry-leading Cloud FinOps platforms.
We have the backing of a global institutional investor and raised pre-seed funding.

Role Overview

We are hiring a Senior AI Engineer to lead our advancements in developing machine learning models, especially in time series analysis and generative AI. This key role involves deep expertise in data analysis, model deployment & optimization, and monitoring them in production. The successful candidate will drive our AI strategy, transforming complex datasets into actionable insights and state-of-the-art solutions.

Key Responsibilities:

1. Data Analysis & Visualization: Perform data mining and cleaning to uncover patterns and insights, using statistical methods and visualization tools for exploratory analysis to identify trends, anomalies, and relationships. Implement methods for knowledge representation & retrieval, and create detailed reports and visualizations for stakeholders to support strategic decisions.
2. Model Development & MLOps: Develop, deploy, and scale machine learning models, ensuring scalability and efficient data management. Implement feature stores, MLOps & monitor model performance in production, optimize for latency and cost, and integrate models into existing software infrastructure.
3. Time Series Analysis: Conduct advanced time series analysis to identify trends and develop forecasting models using methodologies like traditional methods like ARIMA and LSTM and cutting-edge generative time models like Amazon Chronos, Lag Llama, Time LLM, etc, to detect anomalies. Refine models for root cause analysis and collaborate with teams to integrate insights into product and business strategies.
4. Generative AI: Fine-tune Large Language Models for specific use cases using advanced techniques like QLoRA and PEFT, employ transfer learning and reinforcement learning for continuous improvement, and conduct benchmarking studies. Utilize advanced prompting and vector indices to enhance model output relevance and accuracy.

Qualifications:

• Bachelorʼs or Masterʼs degree in Computer Science, Data Science, Engineering, or a related field.
• Advanced proficiency in Python & libraries (pandas, matplotlib etc) and familiarity with other programming languages as needed for model development and data analysis.:
• Proven experience in data mining, data cleaning, and exploratory data analysis.
• Strong foundation in machine learning, with particular focus on time series analysis, NLP, generative AI.
• Proficiency in machine learning frameworks and tools (e.g., TensorFlow, PyTorch, Keras).
• Demonstrated expertise in comprehensive time series analysis, including designing and implementing a wide array of forecasting models, anomaly detection etc using statistical and ML methodologies.
• Proficient understanding of MLOps practices, encompassing the entire lifecycle of model management from deployment and monitoring to optimization and continuous improvement across multiple environments.
• Expertise of MLOps tools, frameworks & best practices to improve the performance, stability and long-term efficiency of ML Models.
• Excellent communication skills with the ability to create detailed reports and visualizations, and effectively communicate findings to both technical and non-technical stakeholders.

Preferred:

• Extensive experience with models involving time series analysis, anomaly detection.
• Experience working in multiple cloud providers likes AWS, GCP Azure, and knowledge of cloud cost optimisation & performance related aspects of cloud.
• Familiarity with data processing, ETL tools, big data technologies (Hadoop, Spark).
• Experience working with and fine-tuning Large Language and Time Models.